diff --git a/.all-contributorsrc b/.all-contributorsrc index fff50536f..30c5221d8 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -121,7 +121,8 @@ "avatar_url": "https://avatars.githubusercontent.com/u/16029092?v=4", "profile": "https://github.com/shagn", "contributions": [ - "bug" + "bug", + "doc" ] }, { @@ -258,6 +259,33 @@ "contributions": [ "code" ] + }, + { + "login": "yibenhuang", + "name": "Yiben Huang", + "avatar_url": "https://avatars.githubusercontent.com/u/62163340?v=4", + "profile": "https://github.com/yibenhuang", + "contributions": [ + "doc" + ] + }, + { + "login": "andrewgross", + "name": "Andrew Gross", + "avatar_url": "https://avatars.githubusercontent.com/u/370118?v=4", + "profile": "https://github.com/andrewgross", + "contributions": [ + "doc" + ] + }, + { + "login": "taniishkaaa", + "name": "taniishkaaa", + "avatar_url": "https://avatars.githubusercontent.com/u/109246904?v=4", + "profile": "https://github.com/taniishkaaa", + "contributions": [ + "doc" + ] } ], "contributorsPerLine": 7, diff --git a/.github/release-drafter.yml b/.github/release-drafter.yml index 7edd7efb9..1d25e449c 100644 --- a/.github/release-drafter.yml +++ b/.github/release-drafter.yml @@ -14,6 +14,7 @@ categories: - title: 'Enhancement' label: 'enhancement' change-template: '- $TITLE @$AUTHOR (#$NUMBER)' +commitish: main template: | ## Changes $CHANGES diff --git a/.gitignore b/.gitignore index 44f997cf7..adba6ab4f 100644 --- a/.gitignore +++ b/.gitignore @@ -7,6 +7,7 @@ docs/_site build dist .vscode +.idea *.gif *.csv */data/* diff --git a/README.md b/README.md index 181d12bf2..e9bc88a3b 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Nixtla   [![Tweet](https://img.shields.io/twitter/url/http/shields.io.svg?style=social)](https://twitter.com/intent/tweet?text=Statistical%20Forecasting%20Algorithms%20by%20Nixtla%20&url=https://github.com/Nixtla/statsforecast&via=nixtlainc&hashtags=StatisticalModels,TimeSeries,Forecasting)  [![Slack](https://img.shields.io/badge/Slack-4A154B?&logo=slack&logoColor=white)](https://join.slack.com/t/nixtlacommunity/shared_invite/zt-1pmhan9j5-F54XR20edHk0UtYAPcW4KQ) -[![All Contributors](https://img.shields.io/badge/all_contributors-28-orange.svg?style=flat-square)](#contributors-) +[![All Contributors](https://img.shields.io/badge/all_contributors-31-orange.svg?style=flat-square)](#contributors-)
@@ -34,7 +34,7 @@ conda install -c conda-forge statsforecast ``` -Vist our [Installation Guide](https://nixtla.github.io/statsforecast/examples/installation.html) for further instructions. +Vist our [Installation Guide](https://nixtla.github.io/statsforecast/docs/getting-started/installation.html) for further instructions. ## Quick Start @@ -53,9 +53,9 @@ sf.fit(df) sf.predict(h=12, level=[95]) ``` -**Get Started with this [quick guide](https://nixtla.github.io/statsforecast/examples/getting_started_short.html).** +**Get Started with this [quick guide](https://nixtla.github.io/statsforecast/docs/getting-started/getting_started_short.html).** -**Follow this [end-to-end walkthrough](https://nixtla.github.io/statsforecast/examples/getting_started_complete.html) for best practices.** +**Follow this [end-to-end walkthrough](https://nixtla.github.io/statsforecast/docs/getting-started/getting_started_complete.html) for best practices.** ## Why? @@ -87,19 +87,19 @@ Missing something? Please open an issue or write us in [![Slack](https://img.shi ## Examples and Guides -πŸ“š [End to End Walkthrough](https://nixtla.github.io/statsforecast/examples/getting_started_complete.html): Model training, evaluation and selection for multiple time series +πŸ“š [End to End Walkthrough](https://nixtla.github.io/statsforecast/docs/getting-started/getting_started_complete.html): Model training, evaluation and selection for multiple time series -πŸ”Ž [Anomaly Detection](https://nixtla.github.io/statsforecast/examples/anomalydetection.html): detect anomalies for time series using in-sample prediction intervals. +πŸ”Ž [Anomaly Detection](https://nixtla.github.io/statsforecast/docs/tutorials/anomalydetection.html): detect anomalies for time series using in-sample prediction intervals. -πŸ‘©β€πŸ”¬ [Cross Validation](https://nixtla.github.io/statsforecast/examples/crossvalidation.html): robust model’s performance evaluation. +πŸ‘©β€πŸ”¬ [Cross Validation](https://nixtla.github.io/statsforecast/docs/tutorials/crossvalidation.html): robust model’s performance evaluation. -❄️ [Multiple Seasonalities](https://nixtla.github.io/statsforecast/examples/multipleseasonalities.html): how to forecast data with multiple seasonalities using an MSTL. +❄️ [Multiple Seasonalities](https://nixtla.github.io/statsforecast/docs/tutorials/multipleseasonalities.html): how to forecast data with multiple seasonalities using an MSTL. -πŸ”Œ [Predict Demand Peaks](https://nixtla.github.io/statsforecast/examples/electricitypeakforecasting.html): electricity load forecasting for detecting daily peaks and reducing electric bills. +πŸ”Œ [Predict Demand Peaks](https://nixtla.github.io/statsforecast/docs/tutorials/electricitypeakforecasting.html): electricity load forecasting for detecting daily peaks and reducing electric bills. -πŸ“ˆ [Intermittent Demand](https://nixtla.github.io/statsforecast/examples/intermittentdata.html): forecast series with very few non-zero observations. +πŸ“ˆ [Intermittent Demand](https://nixtla.github.io/statsforecast/docs/tutorials/intermittentdata.html): forecast series with very few non-zero observations. -🌑️ [Exogenous Regressors](https://nixtla.github.io/statsforecast/examples/exogenous.html): like weather or prices +🌑️ [Exogenous Regressors](https://nixtla.github.io/statsforecast/docs/how-to-guides/exogenous.html): like weather or prices ## Models @@ -109,43 +109,43 @@ Automatic forecasting tools search for the best parameters and select the best p |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| -|[AutoARIMA](https://nixtla.github.io/statsforecast/models.html#autoarima)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[AutoETS](https://nixtla.github.io/statsforecast/models.html#autoets)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[AutoCES](https://nixtla.github.io/statsforecast/models.html#autoces)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[AutoTheta](https://nixtla.github.io/statsforecast/models.html#autotheta)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[AutoARIMA](https://nixtla.github.io/statsforecast/src/core/models.html#autoarima)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[AutoETS](https://nixtla.github.io/statsforecast/src/core/models.html#autoets)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[AutoCES](https://nixtla.github.io/statsforecast/src/core/models.html#autoces)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[AutoTheta](https://nixtla.github.io/statsforecast/src/core/models.html#autotheta)|βœ…|βœ…|βœ…|βœ…|βœ…| ## ARIMA Family These models exploit the existing autocorrelations in the time series. |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| -|[ARIMA](https://nixtla.github.io/statsforecast/models.html#arima)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[AutoRegressive](https://nixtla.github.io/statsforecast/models.html#autoregressive)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[ARIMA](https://nixtla.github.io/statsforecast/src/core/models.html#arima)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[AutoRegressive](https://nixtla.github.io/statsforecast/src/core/models.html#autoregressive)|βœ…|βœ…|βœ…|βœ…|βœ…| ### Theta Family Fit two theta lines to a deseasonalized time series, using different techniques to obtain and combine the two theta lines to produce the final forecasts. |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| -|[Theta](https://nixtla.github.io/statsforecast/models.html#theta)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[OptimizedTheta](https://nixtla.github.io/statsforecast/models.html#optimizedtheta)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[DynamicTheta](https://nixtla.github.io/statsforecast/models.html#dynamictheta)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[DynamicOptimizedTheta](https://nixtla.github.io/statsforecast/models.html#dynamicoptimizedtheta)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[Theta](https://nixtla.github.io/statsforecast/src/core/models.html#theta)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[OptimizedTheta](https://nixtla.github.io/statsforecast/src/core/models.html#optimizedtheta)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[DynamicTheta](https://nixtla.github.io/statsforecast/src/core/models.html#dynamictheta)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[DynamicOptimizedTheta](https://nixtla.github.io/statsforecast/src/core/models.html#dynamicoptimizedtheta)|βœ…|βœ…|βœ…|βœ…|βœ…| ### Multiple Seasonalities Suited for signals with more than one clear seasonality. Useful for low-frequency data like electricity and logs. |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| -|[MSTL](https://nixtla.github.io/statsforecast/models.html#mstl)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[MSTL](https://nixtla.github.io/statsforecast/src/core/models.html#mstl)|βœ…|βœ…|βœ…|βœ…|βœ…| ### GARCH and ARCH Models Suited for modeling time series that exhibit non-constant volatility over time. The ARCH model is a particular case of GARCH. |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| -|[GARCH](https://nixtla.github.io/statsforecast/models.html#garch)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[ARCH](https://nixtla.github.io/statsforecast/models.html#arch)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[GARCH](https://nixtla.github.io/statsforecast/src/core/models.html#garch)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[ARCH](https://nixtla.github.io/statsforecast/src/core/models.html#arch)|βœ…|βœ…|βœ…|βœ…|βœ…| ### Baseline Models @@ -153,24 +153,24 @@ Classical models for establishing baseline. |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| -|[HistoricAverage](https://nixtla.github.io/statsforecast/models.html#historicaverage)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[Naive](https://nixtla.github.io/statsforecast/models.html#naive)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[RandomWalkWithDrift](https://nixtla.github.io/statsforecast/models.html#randomwalkwithdrift)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[SeasonalNaive](https://nixtla.github.io/statsforecast/models.html#seasonalnaive)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[WindowAverage](https://nixtla.github.io/statsforecast/models.html#windowaverage)|βœ…||||| -|[SeasonalWindowAverage](https://nixtla.github.io/statsforecast/models.html#seasonalwindowaverage)|βœ…||||| +|[HistoricAverage](https://nixtla.github.io/statsforecast/src/core/models.html#historicaverage)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[Naive](https://nixtla.github.io/statsforecast/src/core/models.html#naive)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[RandomWalkWithDrift](https://nixtla.github.io/statsforecast/src/core/models.html#randomwalkwithdrift)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[SeasonalNaive](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalnaive)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[WindowAverage](https://nixtla.github.io/statsforecast/src/core/models.html#windowaverage)|βœ…||||| +|[SeasonalWindowAverage](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalwindowaverage)|βœ…||||| ### Exponential Smoothing Uses a weighted average of all past observations where the weights decrease exponentially into the past. Suitable for data with clear trend and/or seasonality. Use the `SimpleExponential` family for data with no clear trend or seasonality. |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| -|[SimpleExponentialSmoothing](https://nixtla.github.io/statsforecast/models.html#simpleexponentialsmoothing)|βœ…||||| -|[SimpleExponentialSmoothingOptimized](https://nixtla.github.io/statsforecast/models.html#simpleexponentialsmoothingoptimized)|βœ…||||| -|[SeasonalExponentialSmoothing](https://nixtla.github.io/statsforecast/models.html#seasonalexponentialsmoothing)|βœ…||||| -|[SeasonalExponentialSmoothingOptimized](https://nixtla.github.io/statsforecast/models.html#seasonalexponentialsmoothingoptimized)|βœ…||||| -|[Holt](https://nixtla.github.io/statsforecast/models.html#holt)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[HoltWinters](https://nixtla.github.io/statsforecast/models.html#holtwinters)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[SimpleExponentialSmoothing](https://nixtla.github.io/statsforecast/src/core/models.html#simpleexponentialsmoothing)|βœ…||||| +|[SimpleExponentialSmoothingOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#simpleexponentialsmoothingoptimized)|βœ…||||| +|[SeasonalExponentialSmoothing](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalexponentialsmoothing)|βœ…||||| +|[SeasonalExponentialSmoothingOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalexponentialsmoothingoptimized)|βœ…||||| +|[Holt](https://nixtla.github.io/statsforecast/src/core/models.html#holt)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[HoltWinters](https://nixtla.github.io/statsforecast/src/core/models.html#holtwinters)|βœ…|βœ…|βœ…|βœ…|βœ…| ### Sparse or Intermittent @@ -178,12 +178,12 @@ Suited for series with very few non-zero observations |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| -|[ADIDA](https://nixtla.github.io/statsforecast/models.html#adida)|βœ…||||| -|[CrostonClassic](https://nixtla.github.io/statsforecast/models.html#crostonclassic)|βœ…||||| -|[CrostonOptimized](https://nixtla.github.io/statsforecast/models.html#crostonoptimized)|βœ…||||| -|[CrostonSBA](https://nixtla.github.io/statsforecast/models.html#crostonsba)|βœ…||||| -|[IMAPA](https://nixtla.github.io/statsforecast/models.html#imapa)|βœ…||||| -|[TSB](https://nixtla.github.io/statsforecast/models.html#tsb)|βœ…||||| +|[ADIDA](https://nixtla.github.io/statsforecast/src/core/models.html#adida)|βœ…||||| +|[CrostonClassic](https://nixtla.github.io/statsforecast/src/core/models.html#crostonclassic)|βœ…||||| +|[CrostonOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#crostonoptimized)|βœ…||||| +|[CrostonSBA](https://nixtla.github.io/statsforecast/src/core/models.html#crostonsba)|βœ…||||| +|[IMAPA](https://nixtla.github.io/statsforecast/src/core/models.html#imapa)|βœ…||||| +|[TSB](https://nixtla.github.io/statsforecast/src/core/models.html#tsb)|βœ…||||| ## πŸ”¨ How to contribute See [CONTRIBUTING.md](https://github.com/Nixtla/statsforecast/blob/main/CONTRIBUTING.md). @@ -224,7 +224,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Yasslight90
Yasslight90
πŸ€” asinig
asinig
πŸ€” Philip Gillißen
Philip Gillißen
πŸ’» - Sebastian Hagn
Sebastian Hagn
πŸ› + Sebastian Hagn
Sebastian Hagn
πŸ› πŸ“– Han Wang
Han Wang
πŸ’» @@ -245,6 +245,11 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Nick To
Nick To
πŸ’» Kevin Kho
Kevin Kho
πŸ’» + + Yiben Huang
Yiben Huang
πŸ“– + Andrew Gross
Andrew Gross
πŸ“– + taniishkaaa
taniishkaaa
πŸ“– + diff --git a/action_files/test_dask.py b/action_files/test_dask.py index 3d0189076..f19eff405 100644 --- a/action_files/test_dask.py +++ b/action_files/test_dask.py @@ -1,12 +1,24 @@ import dask.dataframe as dd +import pytest from statsforecast.utils import generate_series -from .utils import pipeline +from .utils import pipeline, pipeline_with_level -def test_dask_flow(): - n_series = 2 - horizon = 7 +@pytest.fixture() +def n_series(): + return 2 + +@pytest.fixture() +def sample_data(n_series): series = generate_series(n_series).reset_index() series['unique_id'] = series['unique_id'].astype(str) series = dd.from_pandas(series, npartitions=2) - pipeline(series, n_series, horizon) + return series + +def test_dask_flow(sample_data, n_series): + horizon = 7 + pipeline(sample_data, n_series, horizon) + +def test_dask_flow_with_level(sample_data, n_series): + horizon = 7 + pipeline_with_level(sample_data, n_series, horizon) \ No newline at end of file diff --git a/action_files/test_ray.py b/action_files/test_ray.py index 84e7a0a70..cbc8c6f96 100644 --- a/action_files/test_ray.py +++ b/action_files/test_ray.py @@ -1,14 +1,24 @@ +import pytest import ray from statsforecast.utils import generate_series -from .utils import pipeline +from .utils import pipeline, pipeline_with_level -def test_ray_flow(): - n_series = 2 - horizon = 7 +@pytest.fixture() +def n_series(): + return 2 + +@pytest.fixture() +def sample_data(n_series): series = generate_series(n_series).reset_index() series['unique_id'] = series['unique_id'].astype(str) - ctx = ray.data.context.DatasetContext.get_current() - ctx.use_streaming_executor = False series = ray.data.from_pandas(series).repartition(2) - pipeline(series, n_series, horizon) + return series + +def test_ray_flow(sample_data, n_series): + horizon = 7 + pipeline(sample_data, n_series, horizon) + +def test_ray_flow_with_level(sample_data, n_series): + horizon = 7 + pipeline_with_level(sample_data, n_series, horizon) \ No newline at end of file diff --git a/action_files/test_spark.py b/action_files/test_spark.py index 6c213898a..23e4b8b6d 100644 --- a/action_files/test_spark.py +++ b/action_files/test_spark.py @@ -1,13 +1,26 @@ +import pytest from pyspark.sql import SparkSession from statsforecast.utils import generate_series -from .utils import pipeline +from .utils import pipeline, pipeline_with_level -def test_spark_flow(): +@pytest.fixture() +def n_series(): + return 2 + +@pytest.fixture() +def sample_data(n_series): n_series = 2 - horizon = 7 series = generate_series(n_series).reset_index() series['unique_id'] = series['unique_id'].astype(str) spark = SparkSession.builder.getOrCreate() series = spark.createDataFrame(series).repartition(2, 'unique_id') - pipeline(series, n_series, horizon) + return series + +def test_spark_flow(sample_data, n_series): + horizon = 7 + pipeline(sample_data, n_series, horizon) + +def test_spark_flow_with_level(sample_data, n_series): + horizon = 7 + pipeline_with_level(sample_data, n_series, horizon) diff --git a/action_files/utils.py b/action_files/utils.py index 29d2eddf2..75d6965a7 100644 --- a/action_files/utils.py +++ b/action_files/utils.py @@ -41,7 +41,19 @@ def pipeline(series, n_series, horizon): models=models, freq='D', ) - forecast = fa.as_pandas(sf.forecast(df=series, h=horizon, level=[80, 90])) + forecast = fa.as_pandas(sf.forecast(df=series, h=horizon)) print(forecast) assert forecast.shape == (n_series * horizon, len(models) + 2) +def pipeline_with_level(series, n_series, horizon): + models = [ + AutoARIMA(season_length=7), + ] + sf = StatsForecast( + models=models, + freq='D', + ) + forecast = fa.as_pandas(sf.forecast(df=series, h=horizon, level=[80, 90])) + print(forecast.columns) + expected = ["unique_id","ds","AutoARIMA","AutoARIMA-lo-90","AutoARIMA-hi-90", "AutoARIMA-lo-80","AutoARIMA-hi-80"] + assert forecast.shape == (n_series * horizon, len(expected)) \ No newline at end of file diff --git a/nbs/.gitignore b/nbs/.gitignore index 075b2542a..a1889977c 100644 --- a/nbs/.gitignore +++ b/nbs/.gitignore @@ -1 +1,3 @@ /.quarto/ + +lightning_logs/ \ No newline at end of file diff --git a/nbs/_quarto.yml b/nbs/_quarto.yml index bd24c0ba8..f708a5614 100644 --- a/nbs/_quarto.yml +++ b/nbs/_quarto.yml @@ -24,7 +24,7 @@ website: collapse-below: lg left: - text: "Get Started" - href: docs/getting-started/Getting_Started_short.ipynb + href: docs/getting-started/getting_started_short.html - text: "NixtlaVerse" menu: - text: "StatsForecast ⚑️" diff --git a/nbs/docs/getting-started/Installation.ipynb b/nbs/docs/getting-started/0_Installation.ipynb similarity index 97% rename from nbs/docs/getting-started/Installation.ipynb rename to nbs/docs/getting-started/0_Installation.ipynb index 698fb2375..a58dedf2c 100644 --- a/nbs/docs/getting-started/Installation.ipynb +++ b/nbs/docs/getting-started/0_Installation.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "id": "14f5686c-449b-4376-8c58-fc8141f4b0f8", "metadata": {}, @@ -11,6 +12,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "0f1d1483-6da7-4372-8390-84c9c280109e", "metadata": {}, diff --git a/nbs/docs/getting-started/Getting_Started_short.ipynb b/nbs/docs/getting-started/1_Getting_Started_short.ipynb similarity index 99% rename from nbs/docs/getting-started/Getting_Started_short.ipynb rename to nbs/docs/getting-started/1_Getting_Started_short.ipynb index 1cfa21991..74ab5e824 100644 --- a/nbs/docs/getting-started/Getting_Started_short.ipynb +++ b/nbs/docs/getting-started/1_Getting_Started_short.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -10,13 +11,14 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "`StatsForecast` follows the sklearn model API. For this minimal example, you will create an instance of the StatsForecast class and then call its `fit` and `predict` methods. We recommend this option if speed is not paramount and you want to explore the fitted values and parameters. \n", "\n", ":::{.callout-tip}\n", - "If you want to forecast many series, we recommend using the `forecast` method. Check this [Getting Started with multiple time series](./Getting_Started_multiple.ipynb) guide. \n", + "If you want to forecast many series, we recommend using the `forecast` method. Check this [Getting Started with multiple time series](./2_Getting_Started_complete.ipynb) guide. \n", ":::\n", "\n", "The input to StatsForecast is always a data frame in [long format](https://www.theanalysisfactor.com/wide-and-long-data/) with three columns: `unique_id`, `ds` and `y`:\n", @@ -30,7 +32,7 @@ "\n", "As an example, let’s look at the US Air Passengers dataset. This time series consists of monthly totals of a US airline passengers from 1949 to 1960. The CSV is available [here](https://www.kaggle.com/datasets/chirag19/air-passengers).\n", "\n", - "We assume you have StatsForecast already installed. Check this guide for instructions on [how to install StatsForecast](./Installation.ipynb).\n", + "We assume you have StatsForecast already installed. Check this guide for instructions on [how to install StatsForecast](./0_Installation.ipynb).\n", "\n", "First, we’ll import the data:" ] @@ -39,18 +41,10 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UsageError: unrecognized arguments: hide output\n" - ] - } - ], + "outputs": [], "source": [ - "%%capture #To hide output \n", - "! pip install StatsForecast" + "#| hide\n", + "! pip install statsforecast" ] }, { @@ -148,14 +142,15 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "We fit the model by instantiating a new `StatsForecast` object with its two required parameters:\n", + "https://nixtla.github.io/statsforecast/src/core/models.html\n", + "* `models`: a list of models. Select the models you want from [models](../../src/core/models.ipynb) and import them. For this example, we will use a `AutoARIMA` model. We set `season_length` to 12 because we expect seasonal effects every 12 months. (See: [Seasonal periods](https://robjhyndman.com/hyndsight/seasonal-periods/))\n", "\n", - "* `models`: a list of models. Select the models you want from [models](../models.ipynb) and import them. For this example, we will use a `AutoARIMA` model. We set `season_length` to 12 because we expect seasonal effects every 12 months. (See: [Seasonal periods](https://robjhyndman.com/hyndsight/seasonal-periods/))\n", - "\n", - "* `freq`: a string indicating the frequency of the data. (See [panda's available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).)\n", + "* `freq`: a string indicating the frequency of the data. (See [pandas available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).)\n", "\n", "Any settings are passed into the constructor. Then you call its fit method and pass in the historical data frame.\n", "\n", @@ -184,6 +179,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -297,6 +293,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1741,15 +1738,16 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ ":::{.callout-tip}\n", "## Next Steps\n", "\n", - "* Build and end to end forecasting pipeline following best practices in [End to End Walkthrough](./Getting_Started_complete.ipynb)\n", - "* [Forecast millions of series](./ForecastingAtScale.ipynb) in a scalable cluster in the cloud using Spark and Nixtla\n", - "* [Detect anomalies](./AnomalyDetection.ipynb) in your past observations\n", + "* Build and end-to-end forecasting pipeline following best practices in [End to End Walkthrough](./2_Getting_Started_complete.ipynb)\n", + "* [Forecast millions of series](../how-to-guides/Prophet_spark_m5.ipynb) in a scalable cluster in the cloud using Spark and Nixtla\n", + "* [Detect anomalies](../tutorials/AnomalyDetection.ipynb) in your past observations\n", ":::" ] } diff --git a/nbs/docs/getting-started/2_Getting_Started_complete.ipynb b/nbs/docs/getting-started/2_Getting_Started_complete.ipynb new file mode 100644 index 000000000..8fa53e784 --- /dev/null +++ b/nbs/docs/getting-started/2_Getting_Started_complete.ipynb @@ -0,0 +1,40139 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "\n", + "# This is to render Plotly plots into HTML\n", + "# For more information, see https://quarto.org/docs/interactive/widgets/jupyter.html#plotly\n", + "import plotly.io as pio\n", + "pio.renderers.default = \"plotly_mimetype+notebook_connected\"\n", + "\n", + "import warnings\n", + "warnings.simplefilter('ignore')\n", + "\n", + "import logging\n", + "logging.getLogger('statsforecast').setLevel(logging.ERROR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# End to End Walkthrough\n", + "\n", + "> Model training, evaluation and selection for multiple time series" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ":::{.callout-warning collapse=\"true\"}\n", + "## Prerequesites\n", + "This Guide assumes basic familiarity with StatsForecast. For a minimal example visit the [Quick Start](./1_Getting_Started_short.ipynb).\n", + ":::\n", + "\n", + "Follow this article for a step to step guide on building a production-ready forecasting pipeline for multiple time series. \n", + "\n", + "During this guide you will gain familiary with the core `StatsForecast`class and some relevant methods like `StatsForecast.plot`, `StatsForecast.forecast` and `StatsForecast.cross_validation.`\n", + "\n", + "We will use a classical benchmarking dataset from the M4 competition. The dataset includes time series from different domains like finance, economy and sales. In this example, we will use a subset of the Hourly dataset. \n", + "\n", + "We will model each time series individually. Forecasting at this level is also known as local forecasting. Therefore, you will train a series of models for every unique series and then select the best one. StatsForecast focuses on speed, simplicity, and scalability, which makes it ideal for this task.\n", + "\n", + "\n", + "**Outline:**\n", + "\n", + "1. Install packages.\n", + "1. Read the data.\n", + "2. Explore the data.\n", + "3. Train many models for every unique combination of time series. \n", + "4. Evaluate the model's performance using cross-validation. \n", + "5. Select the best model for every unique time series.\n", + "\n", + ":::{.callout-tip collapse=true}\n", + "## Not Covered in this guide\n", + "\n", + "* Forecasting at scale using clusters on the cloud. \n", + " * [Forecast the M5 Dataset in 5min](../how-to-guides/ETS_ray_m5.ipynb) using Ray clusters.\n", + " * [Forecast the M5 Dataset in 5min](../how-to-guides/Prophet_spark_m5.ipynb) using Spark clusters.\n", + " * Learn how to predict [1M series in less than 30min](https://www.anyscale.com/blog/how-nixtla-uses-ray-to-accurately-predict-more-than-a-million-time-series).\n", + "\n", + "* Training models on Multiple Seasonalities. \n", + " * Learn to use multiple seasonality in this [Electricity Load forecasting](../tutorials/ElectricityLoadForecasting.ipynb) tutorial.\n", + "\n", + "* Using external regressors or exogenous variables\n", + " * Follow this tutorial to [include exogenous variables](../how-to-guides/Exogenous.ipynb) like weather or holidays or static variables like category or family. \n", + "\n", + "* Comparing StatsForecast with other popular libraries.\n", + " * You can reproduce our benchmarks [here](https://github.com/Nixtla/statsforecast/tree/main/experiments).\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Install libraries\n", + "\n", + "We assume you have StatsForecast already installed. Check this guide for instructions on [how to install StatsForecast](./0_Installation.ipynb).\n", + "\n", + "Additionally, we will install `s3fs` to read from the S3 Filesystem of AWS and `datasetsforecast` for common error metrics like MAE or MASE.\n", + "\n", + "Install the necessary packages using `pip install statsforecast s3fs datasetsforecast`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: statsforecast in /Users/kevinkho/Work/statsforecast (1.5.0)\n", + "Requirement already satisfied: s3fs in /opt/anaconda3/envs/nixtla/lib/python3.8/site-packages (2023.5.0)\n", + "Requirement already satisfied: datasetsforecast in /opt/anaconda3/envs/nixtla/lib/python3.8/site-packages (0.0.8)\n", + "Requirement already satisfied: matplotlib in /opt/anaconda3/envs/nixtla/lib/python3.8/site-packages (from statsforecast) (3.7.1)\n", + "Requirement already 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hide\n", + "! pip install statsforecast s3fs datasetsforecast" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read the data\n", + "\n", + "We will use pandas to read the M4 Hourly data set stored in a parquet file for efficiency. You can use ordinary pandas operations to read your data in other formats likes `.csv`. \n", + "\n", + "The input to StatsForecast is always a data frame in [long format](https://www.theanalysisfactor.com/wide-and-long-data/) with three columns: `unique_id`, `ds` and `y`:\n", + "\n", + "* The `unique_id` (string, int or category) represents an identifier for the series. \n", + "\n", + "* The `ds` (datestamp or int) column should be either an integer indexing time or a datestampe ideally like YYYY-MM-DD for a date or YYYY-MM-DD HH:MM:SS for a timestamp.\n", + "\n", + "* The `y` (numeric) represents the measurement we wish to forecast. The target column needs to be renamed to `y` if it has a different column name.\n", + "\n", + "This data set already satisfies the requirements. \n", + "\n", + "Depending on your internet connection, this step should take around 10 seconds. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " unique_id ds y\n", + "0 H1 1 605.0\n", + "1 H1 2 586.0\n", + "2 H1 3 586.0\n", + "3 H1 4 559.0\n", + "4 H1 5 511.0" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "Y_df = pd.read_parquet('https://datasets-nixtla.s3.amazonaws.com/m4-hourly.parquet')\n", + "\n", + "Y_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This dataset contains 414 unique series with 900 observations on average. For this example and reproducibility's sake, we will select only 10 unique IDs and keep only the last week. Depending on your processing infrastructure feel free to select more or less series. \n", + "\n", + ":::{.callout-note}\n", + "Processing time is dependent on the available computing resources. Running this example with the complete dataset takes around 10 minutes in a c5d.24xlarge (96 cores) instance from AWS.\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "uids = Y_df['unique_id'].unique()[:10] # Select 10 ids to make the example faster\n", + "\n", + "Y_df = Y_df.query('unique_id in @uids') \n", + "\n", + "Y_df = Y_df.groupby('unique_id').tail(7 * 24) #Select last 7 days of data to make example faster\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Explore Data with the plot method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot some series using the `plot` method from the `StatsForecast` class. This method prints 8 random series from the dataset and is useful for basic EDA." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ":::{.callout-note}\n", + "The `StatsForecast.plot` method uses Plotly as a defaul engine. You can change to MatPlotLib by setting `engine=\"matplotlib\"`. \n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "legendgroup": "y", + "line": { + "color": "#1f77b4", + "width": 1 + }, + "mode": "lines", + "name": "y", + "showlegend": true, + "type": "scatter", + "x": [ + 581, + 582, + 583, + 584, + 585, + 586, + 587, + 588, + 589, + 590, + 591, + 592, + 593, + 594, + 595, + 596, + 597, + 598, + 599, + 600, + 601, + 602, + 603, + 604, + 605, + 606, + 607, + 608, + 609, + 610, + 611, + 612, + 613, + 614, + 615, + 616, + 617, + 618, + 619, + 620, + 621, + 622, + 623, + 624, + 625, + 626, + 627, + 628, + 629, + 630, + 631, + 632, + 633, + 634, + 635, + 636, + 637, + 638, + 639, + 640, + 641, 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from statsforecast import StatsForecast\n", + "\n", + "StatsForecast.plot(Y_df) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train multiple models for many series\n", + "\n", + "StatsForecast can train many models on many time series efficiently. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Start by importing and instantiating the desired models. StatsForecast offers a wide variety of models grouped in the following categories:\n", + "\n", + "* **Auto Forecast:** Automatic forecasting tools search for the best parameters and select the best possible model for a series of time series. These tools are useful for large collections of univariate time series. Includes automatic versions of: Arima, ETS, Theta, CES.\n", + "\n", + "* **Exponential Smoothing:** Uses a weighted average of all past observations where the weights decrease exponentially into the past. Suitable for data with no clear trend or seasonality. Examples: SES, Holt's Winters, SSO.\n", + "\n", + "* **Benchmark models:** classical models for establishing baselines. Examples: Mean, Naive, Random Walk\n", + "\n", + "* **Intermittent or Sparse models:** suited for series with very few non-zero observations. Examples: CROSTON, ADIDA, IMAPA\n", + "\n", + "* **Multiple Seasonalities:** suited for signals with more than one clear seasonality. Useful for low-frequency data like electricity and logs. Examples: MSTL. \n", + "\n", + "* **Theta Models:** fit two theta lines to a deseasonalized time series, using different techniques to obtain and combine the two theta lines to produce the final forecasts. Examples: Theta, DynamicTheta\n", + "\n", + "Here you can check the complete list of [models](../../src/core/models.ipynb) .\n", + "\n", + "For this example we will use:\n", + "\n", + "* `AutoARIMA`: Automatically selects the best ARIMA (AutoRegressive Integrated Moving Average) model using an information criterion. Ref: `AutoARIMA`.\n", + "\n", + "* `HoltWinters`: triple exponential smoothing, Holt-Winters' method is an extension of exponential smoothing for series that contain both trend and seasonality. Ref: `HoltWinters`\n", + "\n", + "* `SeasonalNaive`: Memory Efficient Seasonal Naive predictions. Ref: `SeasonalNaive`\n", + "\n", + "* `HistoricAverage`: arthimetic mean. Ref: `HistoricAverage`.\n", + "\n", + "* `DynamicOptimizedTheta`: The theta family of models has been shown to perform well in various datasets such as M3. Models the deseasonalized time series. Ref: `DynamicOptimizedTheta`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import and instantiate the models. Setting the `season_length` argument is sometimes tricky. This article on [Seasonal periods](https://robjhyndman.com/hyndsight/seasonal-periods/)) by the master, Rob Hyndmann, can be useful. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from statsforecast.models import (\n", + " AutoARIMA,\n", + " HoltWinters,\n", + " CrostonClassic as Croston, \n", + " HistoricAverage,\n", + " DynamicOptimizedTheta as DOT,\n", + " SeasonalNaive\n", + ")\n", + "\n", + "\n", + "# Create a list of models and instantiation parameters\n", + "models = [\n", + " AutoARIMA(season_length=24),\n", + " HoltWinters(),\n", + " Croston(),\n", + " SeasonalNaive(season_length=24),\n", + " HistoricAverage(),\n", + " DOT(season_length=24)\n", + "]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We fit the models by instantiating a new `StatsForecast` object with the following parameters:\n", + "\n", + "* `models`: a list of models. Select the models you want from [models](../../src/core/models.ipynb) and import them.\n", + "\n", + "* `freq`: a string indicating the frequency of the data. (See [pandas available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).)\n", + "\n", + "* `n_jobs`: n_jobs: int, number of jobs used in the parallel processing, use -1 for all cores.\n", + "\n", + "* `fallback_model`: a model to be used if a model fails. \n", + "\n", + "\n", + "Any settings are passed into the constructor. Then you call its fit method and pass in the historical data frame.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Instantiate StatsForecast class as sf\n", + "sf = StatsForecast(\n", + " df=Y_df, \n", + " models=models,\n", + " freq='H', \n", + " n_jobs=-1,\n", + " fallback_model = SeasonalNaive(season_length=7)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ":::{.callout-note}\n", + "StatsForecast achieves its blazing speed using JIT compiling through Numba. The first time you call the statsforecast class, the fit method should take around 5 seconds. The second time -once Numba compiled your settings- it should take less than 0.2s. \n", + ":::" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "The `forecast` method takes two arguments: forecasts next `h` (horizon) and `level`.\n", + "\n", + "* `h` (int): represents the forecast h steps into the future. In this case, 12 months ahead. \n", + "\n", + "* `level` (list of floats): this optional parameter is used for probabilistic forecasting. Set the `level` (or confidence percentile) of your prediction interval. For example, `level=[90]` means that the model expects the real value to be inside that interval 90% of the times. \n", + "\n", + "The forecast object here is a new data frame that includes a column with the name of the model and the y hat values, as well as columns for the uncertainty intervals. Depending on your computer, this step should take around 1min. (If you want to speed things up to a couple of seconds, remove the AutoModels like ARIMA and Theta)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ":::{.callout-note}\n", + "The `forecast` method is compatible with distributed clusters, so it does not store any model parameters. If you want to store parameters for every model you can use the `fit` and `predict` methods. However, those methods are not defined for distrubed engines like Spark, Ray or Dask.\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " ds AutoARIMA AutoARIMA-lo-90 AutoARIMA-hi-90 HoltWinters \\\n", + "unique_id \n", + "H1 749 592.461792 572.325623 612.597961 829.0 \n", + "H1 750 527.174316 495.321777 559.026855 807.0 \n", + "H1 751 488.418549 445.535583 531.301514 785.0 \n", + "H1 752 452.284454 400.677155 503.891785 756.0 \n", + "H1 753 433.127563 374.070984 492.184143 719.0 \n", + "\n", + " HoltWinters-lo-90 HoltWinters-hi-90 CrostonClassic \\\n", + "unique_id \n", + "H1 -246.367554 1904.367554 708.21405 \n", + "H1 -268.367554 1882.367554 708.21405 \n", + "H1 -290.367554 1860.367554 708.21405 \n", + "H1 -319.367554 1831.367554 708.21405 \n", + "H1 -356.367554 1794.367554 708.21405 \n", + "\n", + " SeasonalNaive SeasonalNaive-lo-90 SeasonalNaive-hi-90 \\\n", + "unique_id \n", + "H1 635.0 537.471191 732.528809 \n", + "H1 572.0 474.471222 669.528809 \n", + "H1 532.0 434.471222 629.528809 \n", + "H1 493.0 395.471222 590.528809 \n", + "H1 477.0 379.471222 574.528809 \n", + "\n", + " HistoricAverage HistoricAverage-lo-90 HistoricAverage-hi-90 \\\n", + "unique_id \n", + "H1 660.982117 398.03775 923.926514 \n", + "H1 660.982117 398.03775 923.926514 \n", + "H1 660.982117 398.03775 923.926514 \n", + "H1 660.982117 398.03775 923.926514 \n", + "H1 660.982117 398.03775 923.926514 \n", + "\n", + " DynamicOptimizedTheta DynamicOptimizedTheta-lo-90 \\\n", + "unique_id \n", + "H1 592.701843 577.677307 \n", + "H1 525.589111 505.449738 \n", + "H1 489.251801 462.072876 \n", + "H1 456.195038 430.554291 \n", + "H1 436.290527 411.051239 \n", + "\n", + " DynamicOptimizedTheta-hi-90 \n", + "unique_id \n", + "H1 611.652649 \n", + "H1 546.621826 \n", + "H1 512.424133 \n", + "H1 478.260956 \n", + "H1 461.815948 " + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forecasts_df = sf.forecast(h=48, level=[90])\n", + "\n", + "forecasts_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the results of 8 random series using the `StatsForecast.plot` method. 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Explore other models \n", + "sf.plot(Y_df, forecasts_df, models=[\"AutoARIMA\"], unique_ids=[\"H10\", \"H105\"], level=[90])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate the model's performance\n", + "\n", + "\n", + "In previous steps, we've taken our historical data to predict the future. However, to asses its accuracy we would also like to know how the model would have performed in the past. To assess the accuracy and robustness of your models on your data perform Cross-Validation.\n", + "\n", + "With time series data, **Cross Validation** is done by defining a sliding window across the historical data and predicting the period following it. This form of cross-validation allows us to arrive at a better estimation of our model's predictive abilities across a wider range of temporal instances while also keeping the data in the training set contiguous as is required by our models.\n", + "\n", + "The following graph depicts such a Cross Validation Strategy:\n", + "\n", + "![](https://raw.githubusercontent.com/Nixtla/statsforecast/main/nbs/imgs/ChainedWindows.gif)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cross-validation of time series models is considered a best practice but most implementations are very slow. The statsforecast library implements cross-validation as a distributed operation, making the process less time-consuming to perform. If you have big datasets you can also perform Cross Validation in a distributed cluster using Ray, Dask or Spark. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case, we want to evaluate the performance of each model for the last 2 days (n_windows=2), forecasting every second day (step_size=48). Depending on your computer, this step should take around 1 min. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ":::{.callout-tip}\n", + "Setting `n_windows=1` mirrors a traditional train-test split with our historical data serving as the training set and the last 48 hours serving as the testing set. \n", + ":::" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `cross_validation` method from the `StatsForecast` class takes the following arguments.\n", + "\n", + "* `df`: training data frame \n", + "\n", + "* `h` (int): represents h steps into the future that are being forecasted. In this case, 24 hours ahead. \n", + "\n", + "* `step_size` (int): step size between each window. In other words: how often do you want to run the forecasting processes. \n", + "\n", + "* `n_windows`(int): number of windows used for cross validation. In other words: what number of forecasting processes in the past do you want to evaluate.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "crossvaldation_df = sf.cross_validation(\n", + " df=Y_df,\n", + " h=24,\n", + " step_size=24,\n", + " n_windows=2\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The crossvaldation_df object is a new data frame that includes the following columns:\n", + "\n", + "* `unique_id` index: (If you dont like working with index just run `forecasts_cv_df.resetindex()`)\n", + "\n", + "* `ds`: datestamp or temporal index \n", + "\n", + "* `cutoff`: the last datestamp or temporal index for the `n_windows.` If `n_windows=1`, then one unique cuttoff value, if `n_windows=2` then two unique cutoff values. \n", + "\n", + "* `y`: true value \n", + "\n", + "* `\"model\"`: columns with the model's name and fitted value. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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dscutoffyAutoARIMAHoltWintersCrostonClassicSeasonalNaiveHistoricAverageDynamicOptimizedTheta
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" + ], + "text/plain": [ + " ds cutoff y AutoARIMA HoltWinters CrostonClassic \\\n", + "unique_id \n", + "H1 701 700 619.0 603.925415 847.0 742.668762 \n", + "H1 702 700 565.0 507.591736 820.0 742.668762 \n", + "H1 703 700 532.0 481.281677 790.0 742.668762 \n", + "H1 704 700 495.0 444.410248 784.0 742.668762 \n", + "H1 705 700 481.0 421.168762 752.0 742.668762 \n", + "\n", + " SeasonalNaive HistoricAverage DynamicOptimizedTheta \n", + "unique_id \n", + "H1 691.0 661.674988 612.767517 \n", + "H1 618.0 661.674988 536.846252 \n", + "H1 563.0 661.674988 497.824280 \n", + "H1 529.0 661.674988 464.723236 \n", + "H1 504.0 661.674988 440.972351 " + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crossvaldation_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will evaluate the performance of every model for every series using common error metrics like Mean Absolute Error (MAE) or Mean Square Error (MSE)\n", + "Define a utility function to evaluate different error metrics for the cross validation data frame. \n", + "\n", + "First import the desired error metrics from `datasetsforecast.losses`. Then define a utility function that takes a cross-validation data frame as a metric and returns an evaluation data frame with the average of the error metric for every unique id and fitted model and all cutoffs. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from datasetsforecast.losses import mse, mae, rmse\n", + "\n", + "\n", + "def evaluate_cross_validation(df, metric):\n", + " models = df.drop(columns=['ds', 'cutoff', 'y']).columns.tolist()\n", + " evals = []\n", + " for model in models:\n", + " eval_ = df.groupby(['unique_id', 'cutoff']).apply(lambda x: metric(x['y'].values, x[model].values)).to_frame() # Calculate loss for every unique_id, model and cutoff.\n", + " eval_.columns = [model]\n", + " evals.append(eval_)\n", + " evals = pd.concat(evals, axis=1)\n", + " evals = evals.groupby(['unique_id']).mean(numeric_only=True) # Averages the error metrics for all cutoffs for every combination of model and unique_id\n", + " evals['best_model'] = evals.idxmin(axis=1)\n", + " return evals" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + ":::{.callout-warning}\n", + "You can also use Mean Average Percentage Error (MAPE), however for granular forecasts, MAPE values are extremely [hard to judge](https://blog.blueyonder.com/mean-absolute-percentage-error-mape-has-served-its-duty-and-should-now-retire/) and not useful to assess forecasting quality.\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the data frame with the results of the evaluation of your cross-validation data frame using a Mean Squared Error metric. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AutoARIMAHoltWintersCrostonClassicSeasonalNaiveHistoricAverageDynamicOptimizedThetabest_model
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" + ], + "text/plain": [ + " AutoARIMA HoltWinters CrostonClassic SeasonalNaive \\\n", + "unique_id \n", + "H1 1979.302490 44888.019531 28038.736328 1422.666748 \n", + "H10 458.892700 2812.916504 1483.484131 96.895828 \n", + "H100 8629.948242 121625.375000 91945.140625 12019.000000 \n", + "H101 6818.349121 28453.394531 16183.634766 10944.458008 \n", + "H102 65489.968750 232924.843750 132655.296875 12699.896484 \n", + "\n", + " HistoricAverage DynamicOptimizedTheta best_model \n", + "unique_id \n", + "H1 20927.664062 1296.333984 DynamicOptimizedTheta \n", + "H10 1980.367432 379.621124 SeasonalNaive \n", + "H100 78491.187500 21699.648438 AutoARIMA \n", + "H101 18208.404297 63698.074219 AutoARIMA \n", + "H102 309110.468750 31393.521484 SeasonalNaive " + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluation_df = evaluate_cross_validation(crossvaldation_df, mse)\n", + "\n", + "evaluation_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a summary table with a model column and the number of series where that model performs best. In this case, the Arima and Seasonal Naive are the best models for 10 series and the Theta model should be used for two." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "summary_df = evaluation_df.groupby('best_model').size().sort_values().to_frame()\n", + "\n", + "summary_df.reset_index().columns = [\"Model\", \"Nr. of unique_ids\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can further explore your results by plotting the unique_ids where a specific model wins." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "legendgroup": "y", + "line": { + "color": "#1f77b4", + "width": 1 + }, + "mode": "lines", + "name": "y", + "showlegend": true, + "type": "scatter", + "x": [ + 581, + 582, + 583, + 584, + 585, + 586, + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "seasonal_ids = evaluation_df.query('best_model == \"SeasonalNaive\"').index\n", + "\n", + "sf.plot(Y_df,forecasts_df, unique_ids=seasonal_ids, models=[\"SeasonalNaive\",\"DynamicOptimizedTheta\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Select the best model for every unique series" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define a utility function that takes your forecast's data frame with the predictions and the evaluation data frame and returns a data frame with the best possible forecast for every unique_id." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def get_best_model_forecast(forecasts_df, evaluation_df):\n", + " df = forecasts_df.set_index('ds', append=True).stack().to_frame().reset_index(level=2) # Wide to long \n", + " df.columns = ['model', 'best_model_forecast'] \n", + " df = df.join(evaluation_df[['best_model']])\n", + " df = df.query('model.str.replace(\"-lo-90|-hi-90\", \"\", regex=True) == best_model').copy()\n", + " df.loc[:, 'model'] = [model.replace(bm, 'best_model') for model, bm in zip(df['model'], df['best_model'])]\n", + " df = df.drop(columns='best_model').set_index('model', append=True).unstack()\n", + " df.columns = df.columns.droplevel()\n", + " df = df.reset_index(level=1)\n", + " return df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create your production-ready data frame with the best forecast for every unique_id." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sf.plot(Y_df, prod_forecasts_df, level=[90])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/nbs/docs/getting-started/Getting_Started_complete.ipynb b/nbs/docs/getting-started/Getting_Started_complete.ipynb deleted file mode 100644 index 669a648eb..000000000 --- a/nbs/docs/getting-started/Getting_Started_complete.ipynb +++ /dev/null @@ -1,38230 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# End to End Walkthrough\n", - "\n", - "> Model training, evaluation and selection for multiple time series" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - ":::{.callout-warning collapse=\"true\"}\n", - "## Prerequesites\n", - "This Guide assumes basic familiarity with StatsForecast. For a minimal example visit the [Quick Start](./Getting_Started_short.ipynb)\n", - ":::\n", - "\n", - "Follow this article for a step to step guide on building a production-ready forecasting pipeline for multiple time series. \n", - "\n", - "During this guide you will gain familiary with the core `StatsForecast`class and some relevant methods like `StatsForecast.plot`, `StatsForecast.forecast` and `StatsForecast.cross_validation.`\n", - "\n", - "We will use a classical benchmarking dataset from the M4 competition. The dataset includes time series from different domains like finance, economy and sales. In this example, we will use a subset of the Hourly dataset. \n", - "\n", - "We will model each time series individually. Forecasting at this level is also known as local forecasting. Therefore, you will train a series of models for every unique series and then select the best one. StatsForecast focuses on speed, simplicity, and scalability, which makes it ideal for this task.\n", - "\n", - "\n", - "**Outline:**\n", - "\n", - "1. Install packages.\n", - "1. Read the data.\n", - "2. Explore the data.\n", - "3. Train many models for every unique combination of time series. \n", - "4. Evaluate the model's performance using cross-validation. \n", - "5. Select the best model for every unique time series.\n", - "\n", - ":::{.callout-tip collapse=true}\n", - "## Not Covered in this guide\n", - "\n", - "* Forecasting at scale using clusters on the cloud. \n", - " * [Forecast the M5 Dataset in 5min](./ETS_ray_m5.ipynb) using Ray clusters.\n", - " * [Forecast the M5 Dataset in 5min](./Prophet_spark_m5.ipynb) using Spark clusters.\n", - " * Learn how to predict [1M series in less than 30min](https://www.anyscale.com/blog/how-nixtla-uses-ray-to-accurately-predict-more-than-a-million-time-series).\n", - "\n", - "* Training models on Multiple Seasonalities. \n", - " * Learn to use multiple seasonality in this [Electricity Load forecasting](./ElectricityLoadForecasting.ipynb) tutorial.\n", - "\n", - "* Using external regressors or exogenous variables\n", - " * Follow this tutorial to [include exogenous variables](./Exogenous.ipynb) like weather or holidays or static variables like category or family. \n", - "\n", - "* Comparing StatsForecast with other popular libraries.\n", - " * You can reproduce our benchmarks [here](github.com/nixtla/statsforecas/experiments).\n", - ":::" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Install libraries\n", - "\n", - "We assume you have StatsForecast already installed. Check this guide for instructions on [how to install StatsForecast](./Installation.ipynb).\n", - "\n", - "Additionally, we will install `s3fs` to read from the S3 Filesystem of AWS and `datasetsforecast` for common error metrics like MAE or MASE.\n", - "\n", - "Install the necessary packages using `pip install statsforecast s3fs datasetsforecast`\n", - "``" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: statsforecast in ./statsforecast (1.3.1)\n", - "Requirement already satisfied: s3fs in /Users/max.mergenthaler/miniconda/envs/statsforecast/lib/python3.10/site-packages (2022.11.0)\n", - "Requirement already satisfied: datasetsforecast in /Users/max.mergenthaler/miniconda/envs/statsforecast/lib/python3.10/site-packages (0.0.7)\n", - "Requirement already satisfied: matplotlib in /Users/max.mergenthaler/miniconda/envs/statsforecast/lib/python3.10/site-packages (from statsforecast) (3.6.2)\n", - 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You can use ordinary pandas operations to read your data in other formats likes `.csv`. \n", - "\n", - "The input to StatsForecast is always a data frame in [long format](https://www.theanalysisfactor.com/wide-and-long-data/) with three columns: `unique_id`, `ds` and `y`:\n", - "\n", - "* The `unique_id` (string, int or category) represents an identifier for the series. \n", - "\n", - "* The `ds` (datestamp or int) column should be either an integer indexing time or a datestampe ideally like YYYY-MM-DD for a date or YYYY-MM-DD HH:MM:SS for a timestamp.\n", - "\n", - "* The `y` (numeric) represents the measurement we wish to forecast. \n", - "We will rename the \n", - "\n", - "This data set already satisfies the requirement. \n", - "\n", - "Depending on your internet connection, this step should take around 10 seconds. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Running this example with the complete dataset takes around 10 minutes in a c5d.24xlarge (96 cores) instance from AWS.\n", - ":::" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "uids = Y_df['unique_id'].unique()[:10] # Select 10 ids to make the example faster\n", - "\n", - "Y_df = Y_df.query('unique_id in @uids') \n", - "\n", - "Y_df = Y_df.groupby('unique_id').tail(7 * 24) #Select last 7 days of data to make example faster\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Explore Data with the plot method" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot some series using the `plot` method from the `StatsForecast` class. This method prints 8 random series from the dataset and is useful for basic EDA." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - ":::{.callout-note}\n", - "The `StatsForecast.plot` method uses Plotly as a defaul engine. You can change to MatPlotLib by setting `engine=\"matplotlib\"`. \n", - ":::" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/max.mergenthaler/Nixtla/statsforecast/statsforecast/core.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from tqdm.autonotebook import tqdm\n" - ] - }, - { - "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "line": { - "color": "#1f77b4", - "width": 1 - }, - "mode": "lines", - "name": "y", - "showlegend": true, - "type": "scatter", - "x": [ - 581, - 582, - 583, - 584, - 585, - 586, - 587, - 588, - 589, - 590, - 591, - 592, - 593, - 594, - 595, - 596, - 597, - 598, - 599, - 600, - 601, - 602, - 603, - 604, - 605, - 606, - 607, - 608, - 609, - 610, - 611, - 612, - 613, - 614, - 615, - 616, - 617, - 618, - 619, - 620, - 621, - 622, - 623, - 624, - 625, - 626, - 627, - 628, - 629, - 630, - 631, - 632, - 633, - 634, - 635, - 636, - 637, - 638, - 639, - 640, - 641, - 642, - 643, - 644, - 645, - 646, - 647, - 648, - 649, - 650, - 651, - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from statsforecast import StatsForecast\n", - "\n", - "StatsForecast.plot(Y_df) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Train multiple models for many series\n", - "\n", - "StatsForecast can train many models on many time series efficiently. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Start by importing and instantiating the desired models. StatsForecast offers a wide variety of models grouped in the following categories:\n", - "\n", - "* **Auto Forecast:** Automatic forecasting tools search for the best parameters and select the best possible model for a series of time series. These tools are useful for large collections of univariate time series. Includes automatic versions of: Arima, ETS, Theta, CES.\n", - "\n", - "* **Exponential Smoothing:** Uses a weighted average of all past observations where the weights decrease exponentially into the past. Suitable for data with no clear trend or seasonality. Examples: SES, Holt's Winters, SSO.\n", - "\n", - "* **Benchmark models:** classical models for establishing baselines. Examples: Mean, Naive, Random Walk\n", - "\n", - "* **Intermittent or Sparse models:** suited for series with very few non-zero observations. Examples: CROSTON, ADIDA, IMAPA\n", - "\n", - "* **Multiple Seasonalities:** suited for signals with more than one clear seasonality. Useful for low-frequency data like electricity and logs. Examples: MSTL. \n", - "\n", - "* **Theta Models:** fit two theta lines to a deseasonalized time series, using different techniques to obtain and combine the two theta lines to produce the final forecasts. Examples: Theta, DynamicTheta\n", - "\n", - "Here you can check the complete list of [models](../models_intro.qmd).\n", - "\n", - "For this example we will use:\n", - "\n", - "* `AutoARIMA`: Automatically selects the best ARIMA (AutoRegressive Integrated Moving Average) model using an information criterion. Ref: `AutoARIMA`.\n", - "\n", - "* `HoltWinters`: triple exponential smoothing, Holt-Winters' method is an extension of exponential smoothing for series that contain both trend and seasonality. Ref: `HoltWinters`\n", - "\n", - "* `SeasonalNaive`: Memory Efficient Seasonal Naive predictions. Ref: `SeasonalNaive`\n", - "\n", - "* `HistoricAverage`: arthimetic mean. Ref: `HistoricAverage`.\n", - "\n", - "* `DynamicOptimizedTheta`: The theta family of models has been shown to perform well in various datasets such as M3. Models the deseasonalized time series. Ref: `DynamicOptimizedTheta`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import and instantiate the models. Setting the `season_length` argument is sometimes tricky. This article on [Seasonal periods](https://robjhyndman.com/hyndsight/seasonal-periods/)) by the master, Rob Hyndmann, can be useful. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from statsforecast.models import (\n", - " AutoARIMA,\n", - " HoltWinters,\n", - " CrostonClassic as Croston, \n", - " HistoricAverage,\n", - " DynamicOptimizedTheta as DOT,\n", - " SeasonalNaive\n", - ")\n", - "\n", - "\n", - "# Create a list of models and instantiation parameters\n", - "models = [\n", - " AutoARIMA(season_length=24),\n", - " HoltWinters(),\n", - " Croston(),\n", - " SeasonalNaive(season_length=24),\n", - " HistoricAverage(),\n", - " DOT(season_length=24)\n", - "]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We fit the models by instantiating a new `StatsForecast` object with the following parameters:\n", - "\n", - "* `models`: a list of models. Select the models you want from [models](../models.ipynb) and import them.\n", - "\n", - "* `freq`: a string indicating the frequency of the data. (See [panda's available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).)\n", - "\n", - "* `n_jobs`: n_jobs: int, number of jobs used in the parallel processing, use -1 for all cores.\n", - "\n", - "* `fallback_model`: a model to be used if a model fails. \n", - "\n", - "\n", - "Any settings are passed into the constructor. Then you call its fit method and pass in the historical data frame.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Instantiate StatsForecast class as sf\n", - "sf = StatsForecast(\n", - " df=Y_df, \n", - " models=models,\n", - " freq='H', \n", - " n_jobs=-1,\n", - " fallback_model = SeasonalNaive(season_length=7)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - ":::{.callout-note}\n", - "StatsForecast achieves its blazing speed using JIT compiling through Numba. The first time you call the statsforecast class, the fit method should take around 5 seconds. The second time -once Numba compiled your settings- it should take less than 0.2s. \n", - ":::" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "The `forecast` method takes two arguments: forecasts next `h` (horizon) and `level`.\n", - "\n", - "* `h` (int): represents the forecast h steps into the future. In this case, 12 months ahead. \n", - "\n", - "* `level` (list of floats): this optional parameter is used for probabilistic forecasting. Set the `level` (or confidence percentile) of your prediction interval. For example, `level=[90]` means that the model expects the real value to be inside that interval 90% of the times. \n", - "\n", - "The forecast object here is a new data frame that includes a column with the name of the model and the y hat values, as well as columns for the uncertainty intervals. Depending on your computer, this step should take around 1min. (If you want to speed things up to a couple of seconds, remove the AutoModels like ARIMA and Theta)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - ":::{.callout-note}\n", - "The `forecast` method is compatible with distributed clusters, so it does not store any model parameters. If you want to store parameters for every model you can use the `fit` and `predict` methods. However, those methods are not defined for distrubed engines like Spark, Ray or Dask.\n", - ":::" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Explore other models \n", - "sf.plot(Y_df, forecasts_df, models=[\"AutoARIMA\"], unique_ids=[\"H10\", \"H105\"], level=[90])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluate the model's performance\n", - "\n", - "\n", - "In previous steps, we've taken our historical data to predict the future. However, to asses its accuracy we would also like to know how the model would have performed in the past. To assess the accuracy and robustness of your models on your data perform Cross-Validation.\n", - "\n", - "With time series data, **Cross Validation** is done by defining a sliding window across the historical data and predicting the period following it. This form of cross-validation allows us to arrive at a better estimation of our model's predictive abilities across a wider range of temporal instances while also keeping the data in the training set contiguous as is required by our models.\n", - "\n", - "The following graph depicts such a Cross Validation Strategy:\n", - "\n", - "![](https://raw.githubusercontent.com/Nixtla/statsforecast/main/nbs/imgs/ChainedWindows.gif)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Cross-validation of time series models is considered a best practice but most implementations are very slow. The statsforecast library implements cross-validation as a distributed operation, making the process less time-consuming to perform. If you have big datasets you can also perform Cross Validation in a distributed cluster using Ray, Dask or Spark. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case, we want to evaluate the performance of each model for the last 2 days (n_windows=2), forecasting every second day (step_size=48). Depending on your computer, this step should take around 1 min. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - ":::{.callout-tip}\n", - "Setting `n_windows=1` mirrors a traditional train-test split with our historical data serving as the training set and the last 48 hours serving as the testing set. \n", - ":::" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `cross_validation` method from the `StatsForecast` class takes the following arguments.\n", - "\n", - "* `df`: training data frame \n", - "\n", - "* `h` (int): represents h steps into the future that are being forecasted. In this case, 24 hours ahead. \n", - "\n", - "* `step_size` (int): step size between each window. In other words: how often do you want to run the forecasting processes. \n", - "\n", - "* `n_windows`(int): number of windows used for cross validation. In other words: what number of forecasting processes in the past do you want to evaluate.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "crossvaldation_df = sf.cross_validation(\n", - " df=Y_df,\n", - " h=24,\n", - " step_size=24,\n", - " n_windows=2\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The crossvaldation_df object is a new data frame that includes the following columns:\n", - "\n", - "* `unique_id` index: (If you dont like working with index just run `forecasts_cv_df.resetindex()`)\n", - "\n", - "* `ds`: datestamp or temporal index \n", - "\n", - "* `cutoff`: the last datestamp or temporal index for the `n_windows.` If `n_windows=1`, then one unique cuttoff value, if `n_windows=2` then two unique cutoff values. \n", - "\n", - "* `y`: true value \n", - "\n", - "* `\"model\"`: columns with the model's name and fitted value. \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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dscutoffyAutoARIMAHoltWintersCrostonClassicSeasonalNaiveHistoricAverageDynamicOptimizedTheta
unique_id
H1701700619.0603.925415847.0742.668762691.0661.674988612.767517
H1702700565.0507.591736820.0742.668762618.0661.674988536.846252
H1703700532.0481.281677790.0742.668762563.0661.674988497.824280
H1704700495.0444.410248784.0742.668762529.0661.674988464.723236
H1705700481.0421.168762752.0742.668762504.0661.674988440.972351
\n", - "
" - ], - "text/plain": [ - " ds cutoff y AutoARIMA HoltWinters CrostonClassic \\\n", - "unique_id \n", - "H1 701 700 619.0 603.925415 847.0 742.668762 \n", - "H1 702 700 565.0 507.591736 820.0 742.668762 \n", - "H1 703 700 532.0 481.281677 790.0 742.668762 \n", - "H1 704 700 495.0 444.410248 784.0 742.668762 \n", - "H1 705 700 481.0 421.168762 752.0 742.668762 \n", - "\n", - " SeasonalNaive HistoricAverage DynamicOptimizedTheta \n", - "unique_id \n", - "H1 691.0 661.674988 612.767517 \n", - "H1 618.0 661.674988 536.846252 \n", - "H1 563.0 661.674988 497.824280 \n", - "H1 529.0 661.674988 464.723236 \n", - "H1 504.0 661.674988 440.972351 " - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "crossvaldation_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will evaluate the performance of every model for every series using common error metrics like Mean Absolute Error (MAE) or Mean Square Error (MSE)\n", - "Define a utility function to evaluate different error metrics for the cross validation data frame. \n", - "\n", - "First import the desired error metrics from `datasetsforecast.losses`. Then define a utility function that takes a cross-validation data frame as a metric and returns an evaluation data frame with the average of the error metric for every unique id and fitted model and all cutoffs. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from datasetsforecast.losses import mse, mae, rmse\n", - "\n", - "\n", - "def evaluate_cross_validation(df, metric):\n", - " models = df.drop(columns=['ds', 'cutoff', 'y']).columns.tolist()\n", - " evals = []\n", - " for model in models:\n", - " eval_ = df.groupby(['unique_id', 'cutoff']).apply(lambda x: metric(x['y'].values, x[model].values)).to_frame() # Calculate loss for every unique_id, model and cutoff.\n", - " eval_.columns = [model]\n", - " evals.append(eval_)\n", - " evals = pd.concat(evals, axis=1)\n", - " evals = evals.groupby(['unique_id']).mean(numeric_only=True) # Averages the error metrics for all cutoffs for every combination of model and unique_id\n", - " evals['best_model'] = evals.idxmin(axis=1)\n", - " return evals" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - ":::{.callout-warning}\n", - "You can also use Mean Average Percentage Error (MAPE), however for granular forecasts, MAPE values are extremely [hard to judge](https://blog.blueyonder.com/mean-absolute-percentage-error-mape-has-served-its-duty-and-should-now-retire/) and not useful to assess forecasting quality.\n", - ":::" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create the data frame with the results of the evaluation of your cross-validation data frame using a Mean Squared Error metric. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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AutoARIMAHoltWintersCrostonClassicSeasonalNaiveHistoricAverageDynamicOptimizedThetabest_model
unique_id
H11979.30224644888.01953128038.7363281422.66674820927.6640621296.333984DynamicOptimizedTheta
H10458.8927002812.9165041483.48413196.8958281980.367432379.621124SeasonalNaive
H1008629.948242121625.37500091945.14062512019.00000078491.18750021699.648438AutoARIMA
H1016818.34863328453.39453116183.63476610944.45800818208.40429763698.074219AutoARIMA
H10265489.968750232924.843750132655.29687512699.896484309110.46875031393.521484SeasonalNaive
\n", - "
" - ], - "text/plain": [ - " AutoARIMA HoltWinters CrostonClassic SeasonalNaive \\\n", - "unique_id \n", - "H1 1979.302246 44888.019531 28038.736328 1422.666748 \n", - "H10 458.892700 2812.916504 1483.484131 96.895828 \n", - "H100 8629.948242 121625.375000 91945.140625 12019.000000 \n", - "H101 6818.348633 28453.394531 16183.634766 10944.458008 \n", - "H102 65489.968750 232924.843750 132655.296875 12699.896484 \n", - "\n", - " HistoricAverage DynamicOptimizedTheta best_model \n", - "unique_id \n", - "H1 20927.664062 1296.333984 DynamicOptimizedTheta \n", - "H10 1980.367432 379.621124 SeasonalNaive \n", - "H100 78491.187500 21699.648438 AutoARIMA \n", - "H101 18208.404297 63698.074219 AutoARIMA \n", - "H102 309110.468750 31393.521484 SeasonalNaive " - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluation_df = evaluate_cross_validation(crossvaldation_df, mse)\n", - "\n", - "evaluation_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a summary table with a model column and the number of series where that model performs best. In this case, the Arima and Seasonal Naive are the best models for 10 series and the Theta model should be used for two." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "summary_df = evaluation_df.groupby('best_model').size().sort_values().to_frame()\n", - "\n", - "summary_df.reset_index().columns = [\"Model\", \"Nr. of unique_ids\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can further explore your results by plotting the unique_ids where a specific model wins." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "line": { - "color": "#1f77b4", - "width": 1 - }, - "mode": "lines", - "name": "y", - "showlegend": true, - "type": "scatter", - "x": [ - 581, - 582, - 583, - 584, - 585, - 586, - 587, - 588, - 589, - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "seasonal_ids = evaluation_df.query('best_model == \"SeasonalNaive\"').index\n", - "\n", - "sf.plot(Y_df,forecasts_df, unique_ids=seasonal_ids, models=[\"SeasonalNaive\",\"DynamicOptimizedTheta\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Select the best model for every unique series" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Define a utility function that takes your forecast's data frame with the predictions and the evaluation data frame and returns a data frame with the best possible forecast for every unique_id." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def get_best_model_forecast(forecasts_df, evaluation_df):\n", - " df = forecasts_df.set_index('ds', append=True).stack().to_frame().reset_index(level=2) # Wide to long \n", - " df.columns = ['model', 'best_model_forecast'] \n", - " df = df.join(evaluation_df[['best_model']])\n", - " df = df.query('model.str.replace(\"-lo-90|-hi-90\", \"\", regex=True) == best_model').copy()\n", - " df.loc[:, 'model'] = [model.replace(bm, 'best_model') for model, bm in zip(df['model'], df['best_model'])]\n", - " df = df.drop(columns='best_model').set_index('model', append=True).unstack()\n", - " df.columns = df.columns.droplevel()\n", - " df = df.reset_index(level=1)\n", - " return df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create your production-ready data frame with the best forecast for every unique_id." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "model ds best_model best_model-hi-90 best_model-lo-90\n", - "unique_id \n", - "H1 749 592.701843 611.652649 577.677307\n", - "H1 750 525.589111 546.621826 505.449738\n", - "H1 751 489.251801 512.424133 462.072876\n", - "H1 752 456.195038 478.260956 430.554291\n", - "H1 753 436.290527 461.815948 411.051239" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "prod_forecasts_df = get_best_model_forecast(forecasts_df, evaluation_df)\n", - "\n", - "prod_forecasts_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot the results. 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sf.plot(Y_df, prod_forecasts_df, level=[90])" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/nbs/docs/how-to-guides/Automatic_Forecasting.ipynb b/nbs/docs/how-to-guides/00_Automatic_Forecasting.ipynb similarity index 98% rename from nbs/docs/how-to-guides/Automatic_Forecasting.ipynb rename to nbs/docs/how-to-guides/00_Automatic_Forecasting.ipynb index f32074a3f..626948fe7 100644 --- a/nbs/docs/how-to-guides/Automatic_Forecasting.ipynb +++ b/nbs/docs/how-to-guides/00_Automatic_Forecasting.ipynb @@ -117,7 +117,7 @@ "outputs": [], "source": [ "# Generate forecasts for the specified horizon using the sf object\n", - "Y_hat_df = sf.forecast(df=Y_df, horizon=horizon) # forecast data\n", + "Y_hat_df = sf.forecast(df=Y_df, h=horizon) # forecast data\n", "\n", "# Display the first few rows of the forecast DataFrame\n", "Y_hat_df.head() # preview of forecasted data" diff --git a/nbs/docs/getting-started/Getting_Started_complete_polars.ipynb b/nbs/docs/how-to-guides/Getting_Started_complete_polars.ipynb similarity index 100% rename from nbs/docs/getting-started/Getting_Started_complete_polars.ipynb rename to nbs/docs/how-to-guides/Getting_Started_complete_polars.ipynb diff --git a/nbs/docs/how-to-guides/ray.ipynb b/nbs/docs/how-to-guides/ray.ipynb index 87aea5305..aef2a0827 100644 --- a/nbs/docs/how-to-guides/ray.ipynb +++ b/nbs/docs/how-to-guides/ray.ipynb @@ -1,5 +1,19 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "import warnings\n", + "warnings.simplefilter('ignore')\n", + "\n", + "import logging\n", + "logging.getLogger('statsforecast').setLevel(logging.ERROR)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -36,14 +50,6 @@ "execution_count": null, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/kevinkho/Work/statsforecast/statsforecast/core.py:24: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n", - " from tqdm.autonotebook import tqdm\n" - ] - }, { "data": { "text/html": [ @@ -174,13 +180,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-06-13 01:32:22,311\tINFO bulk_executor.py:42 -- Executing DAG InputDataBuffer[Input] -> AllToAllOperator[Repartition]\n" + "2023-06-17 01:39:08,329\tINFO bulk_executor.py:42 -- Executing DAG InputDataBuffer[Input] -> AllToAllOperator[Repartition]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0e3630c98d0e4052a20ef369903a26dc", + "model_id": "fc6c5360ce894209a73dbaee80fa8625", "version_major": 2, "version_minor": 0 }, @@ -195,13 +201,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-06-13 01:32:23,537\tINFO bulk_executor.py:42 -- Executing DAG InputDataBuffer[Input] -> AllToAllOperator[Repartition]\n" + "2023-06-17 01:39:09,554\tINFO bulk_executor.py:42 -- Executing DAG InputDataBuffer[Input] -> AllToAllOperator[Repartition]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fa310b4b38fd49d696b99747deb4d2b1", + "model_id": "aefc889d406b4702a10b22ba0333bceb", "version_major": 2, "version_minor": 0 }, @@ -216,13 +222,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-06-13 01:32:23,636\tINFO bulk_executor.py:42 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[MapBatches(add_simple_key)]\n" + "2023-06-17 01:39:09,727\tINFO bulk_executor.py:42 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[MapBatches(add_simple_key)]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5d8a580d7a564817976e1e70b7649bbe", + "model_id": "9c8724f4896449a58b2146cd7770e8e5", "version_major": 2, "version_minor": 0 }, @@ -237,13 +243,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-06-13 01:32:24,845\tINFO bulk_executor.py:42 -- Executing DAG InputDataBuffer[Input] -> AllToAllOperator[Sort] -> TaskPoolMapOperator[MapBatches(group_fn)]\n" + "2023-06-17 01:39:11,134\tINFO bulk_executor.py:42 -- Executing DAG InputDataBuffer[Input] -> AllToAllOperator[Sort] -> TaskPoolMapOperator[MapBatches(group_fn)]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "402c92e6db3d4fdc9717453328f68218", + "model_id": "3f27993f41714e25a128d9f418f158ee", "version_major": 2, "version_minor": 0 }, @@ -257,7 +263,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "82c33a3e27ee435599ab37e9e7f63c2c", + "model_id": "677a723e3bb144aebedc28fcd68c1493", "version_major": 2, "version_minor": 0 }, @@ -271,7 +277,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1596e91598e745c8bc0469150d0e8be9", + "model_id": "265137f672a54c339c4220719078eac7", "version_major": 2, "version_minor": 0 }, @@ -285,7 +291,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cfe536cf41024040b4426a8452277e88", + "model_id": "5489fcd3398a42cfaf83bb4bb165eed8", "version_major": 2, "version_minor": 0 }, diff --git a/nbs/docs/tutorials/ConformalPrediction.ipynb b/nbs/docs/tutorials/ConformalPrediction.ipynb new file mode 100644 index 000000000..5fb544b5e --- /dev/null +++ b/nbs/docs/tutorials/ConformalPrediction.ipynb @@ -0,0 +1,14171 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "import warnings\n", + "warnings.simplefilter('ignore')\n", + "\n", + "import logging\n", + "logging.getLogger('statsforecast').setLevel(logging.ERROR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Conformal Prediction\n", + "\n", + "> In this example, we'll implement conformal prediction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "::: {.callout-warning collapse=\"true\"}\n", + "\n", + "## Prerequisites\n", + "\n", + "This tutorial assumes basic familiarity with StatsForecast. For a minimal example visit the [Quick Start](../getting-started/1_Getting_Started_short.ipynb) \n", + ":::" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction \n", + "\n", + "When we generate a forecast, we usually produce a single value known as the point forecast. This value, however, doesn't tell us anything about the uncertainty associated with the forecast. To have a measure of this uncertainty, we need **prediction intervals**. \n", + "\n", + "A prediction interval is a range of values that the forecast can take with a given probability. Hence, a 95% prediction interval should contain a range of values that include the actual future value with probability 95%. Probabilistic forecasting aims to generate the full forecast distribution. Point forecasting, on the other hand, usually returns the mean or the median or said distribution. However, in real-world scenarios, it is better to forecast not only the most probable future outcome, but many alternative outcomes as well. \n", + "\n", + "The problem is that some timeseries models provide forecast distributions, but some other ones only provide point forecasts. How can we then estimate the uncertainty of predictions? " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "::: {.callout-important}\n", + "## Prediction Intervals\n", + "For models that already provide the forecast distribution, check [Prediction Intervals](./UncertaintyIntervals.ipynb). \n", + ":::" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conformal Prediction\n", + "\n", + "For a video introduction, see the [PyData Seattle presentation](https://www.youtube.com/watch?v=Bj1U-Rrxk48).\n", + "\n", + "Multi-quantile losses and statistical models can provide provide prediction intervals, but the problem is that these are uncalibrated, meaning that the actual frequency of observations falling within the interval does not align with the confidence level associated with it. For example, a calibrated 95% prediction interval should contain the true value 95% of the time in repeated sampling. An uncalibrated 95% prediction interval, on the other hand, might contain the true value only 80% of the time, or perhaps 99% of the time. In the first case, the interval is too narrow and underestimates the uncertainty, while in the second case, it is too wide and overestimates the uncertainty. \n", + "\n", + "Statistical methods also assume normality. Here, we talk about another method called conformal prediction that doesn't require any distributional assumptions. More information on the approach can be found in [this repo owned by Valery Manokhin](https://github.com/valeman/awesome-conformal-prediction).\n", + "\n", + "Conformal prediction intervals use cross-validation on a point forecaster model to generate the intervals. This means that no prior probabilities are needed, and the output is well-calibrated. No additional training is needed, and the model is treated as a black box. The approach is compatible with any model.\n", + "\n", + "[Statsforecast](https://github.com/nixtla/statsforecast) now supports Conformal Prediction on all available models." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Install libraries \n", + "\n", + "We assume that you have StatsForecast already installed. If not, check this guide for instructions on [how to install StatsForecast](../getting-started/0_Installation.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Install the necessary packages using `pip install statsforecast`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "pip install statsforecast -U" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load and explore the data\n", + "\n", + "For this example, we'll use the hourly dataset from the [M4 Competition](https://www.sciencedirect.com/science/article/pii/S0169207019301128). We first need to download the data from a URL and then load it as a `pandas` dataframe. Notice that we'll load the train and the test data separately. We'll also rename the `y` column of the test data as `y_test`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd \n", + "\n", + "train = pd.read_csv('https://auto-arima-results.s3.amazonaws.com/M4-Hourly.csv')\n", + "test = pd.read_csv('https://auto-arima-results.s3.amazonaws.com/M4-Hourly-test.csv').rename(columns={'y': 'y_test'})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_series = 8 \n", + "uids = train['unique_id'].unique()[:n_series] # select first n_series of the dataset\n", + "train = train.query('unique_id in @uids')\n", + "test = test.query('unique_id in @uids')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot these series using the `statsforecast.plot` method from the [StatsForecast](https://nixtla.github.io/statsforecast/core.html#statsforecast) class. This method has multiple parameters, and the required ones to generate the plots in this notebook are explained below. \n", + "\n", + "- `df`: A `pandas` dataframe with columns [`unique_id`, `ds`, `y`]. \n", + "- `forecasts_df`: A `pandas` dataframe with columns [`unique_id`, `ds`] and models. \n", + "- `plot_random`: bool = `True`. Plots the time series randomly. \n", + "- `models`: List[str]. A list with the models we want to plot. \n", + "- `level`: List[float]. A list with the prediction intervals we want to plot. \n", + "- `engine`: str = `plotly`. It can also be `matplotlib`. `plotly` generates interactive plots, while `matplotlib` generates static plots. 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from statsforecast import StatsForecast\n", + "\n", + "StatsForecast.plot(train, test, plot_random = False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train models\n", + "\n", + "StatsForecast can train multiple [models](https://nixtla.github.io/statsforecast/#models) on different time series efficiently. Most of these models can generate a probabilistic forecast, which means that they can produce both point forecasts and prediction intervals. \n", + "\n", + "For this example, we'll use [SimpleExponentialSmoothing](https://nixtla.github.io/statsforecast/src/core/models.html#simpleexponentialsmoothing) and [ADIDA](https://nixtla.github.io/statsforecast/src/core/models.html#adida) which do not provide a prediction interval natively. Thus, it makes sense to use Conformal Prediction to generate the prediction interval. \n", + "\n", + "We'll also show using it with [ARIMA](https://nixtla.github.io/statsforecast/src/core/models.html#arima) to provide prediction intervals that don't assume normality.\n", + "\n", + "To use these models, we first need to import them from `statsforecast.models` and then we need to instantiate them." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from statsforecast.models import SeasonalExponentialSmoothing, ADIDA, ARIMA\n", + "from statsforecast.utils import ConformalIntervals\n", + "\n", + "# Create a list of models and instantiation parameters \n", + "intervals = ConformalIntervals(h=24, n_windows=2)\n", + "\n", + "models = [\n", + " SeasonalExponentialSmoothing(season_length=24,alpha=0.1, prediction_intervals=intervals),\n", + " ADIDA(prediction_intervals=intervals),\n", + " ARIMA(order=(24,0,12), season_length=24, prediction_intervals=intervals),\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To instantiate a new StatsForecast object, we need the following parameters: \n", + "\n", + "- `df`: The dataframe with the training data. \n", + "- `models`: The list of models defined in the previous step. \n", + "- `freq`: A string indicating the frequency of the data. See [pandas' available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases). \n", + "- `n_jobs`: An integer that indicates the number of jobs used in parallel processing. Use -1 to select all cores. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sf = StatsForecast(\n", + " df=train, \n", + " models=models, \n", + " freq='H', \n", + ") " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we're ready to generate the forecasts and the prediction intervals. To do this, we'll use the `forecast` method, which takes two arguments: \n", + "\n", + "- `h`: An integer that represent the forecasting horizon. In this case, we'll forecast the next 24 hours. \n", + "- `level`: A list of floats with the confidence levels of the prediction intervals. For example, `level=[95]` means that the range of values should include the actual future value with probability 95%. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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unique_iddsSeasonalESSeasonalES-lo-90SeasonalES-lo-80SeasonalES-hi-80SeasonalES-hi-90ADIDAADIDA-lo-90ADIDA-lo-80ADIDA-hi-80ADIDA-hi-90ARIMAARIMA-lo-90ARIMA-lo-80ARIMA-hi-80ARIMA-hi-90
0H1701624.132690561.315369565.365356682.900024686.950012747.292542668.049988672.099976822.485107826.535095634.355164581.760376585.810364682.900024686.950012
1H1702555.698181501.886902510.377441601.018921609.509460747.292542560.200012570.400024924.185059934.385071578.701355540.992310542.581909614.820801616.410400
2H1703514.403015468.656036471.506042557.299988560.150024747.292542546.849976549.700012944.885071947.735107544.308960528.375244531.132568557.485352560.242676
3H1704482.057892438.715790442.315796521.799988525.400024747.292542508.600006512.200012982.385071985.985107516.846619504.739288504.785309528.907959528.953979
4H1705460.222534419.595062422.745056497.700012500.850006747.292542486.149994489.2999881005.2850951008.435059502.623077485.736938488.473846516.772339519.509277
\n", + "
" + ], + "text/plain": [ + " unique_id ds SeasonalES SeasonalES-lo-90 SeasonalES-lo-80 \\\n", + "0 H1 701 624.132690 561.315369 565.365356 \n", + "1 H1 702 555.698181 501.886902 510.377441 \n", + "2 H1 703 514.403015 468.656036 471.506042 \n", + "3 H1 704 482.057892 438.715790 442.315796 \n", + "4 H1 705 460.222534 419.595062 422.745056 \n", + "\n", + " SeasonalES-hi-80 SeasonalES-hi-90 ADIDA ADIDA-lo-90 ADIDA-lo-80 \\\n", + "0 682.900024 686.950012 747.292542 668.049988 672.099976 \n", + "1 601.018921 609.509460 747.292542 560.200012 570.400024 \n", + "2 557.299988 560.150024 747.292542 546.849976 549.700012 \n", + "3 521.799988 525.400024 747.292542 508.600006 512.200012 \n", + "4 497.700012 500.850006 747.292542 486.149994 489.299988 \n", + "\n", + " ADIDA-hi-80 ADIDA-hi-90 ARIMA ARIMA-lo-90 ARIMA-lo-80 \\\n", + "0 822.485107 826.535095 634.355164 581.760376 585.810364 \n", + "1 924.185059 934.385071 578.701355 540.992310 542.581909 \n", + "2 944.885071 947.735107 544.308960 528.375244 531.132568 \n", + "3 982.385071 985.985107 516.846619 504.739288 504.785309 \n", + "4 1005.285095 1008.435059 502.623077 485.736938 488.473846 \n", + "\n", + " ARIMA-hi-80 ARIMA-hi-90 \n", + "0 682.900024 686.950012 \n", + "1 614.820801 616.410400 \n", + "2 557.485352 560.242676 \n", + "3 528.907959 528.953979 \n", + "4 516.772339 519.509277 " + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "levels = [80, 90] # confidence levels of the prediction intervals \n", + "\n", + "forecasts = sf.forecast(h=24, level=levels)\n", + "forecasts = forecasts.reset_index()\n", + "forecasts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot prediction intervals\n", + "\n", + "Here we'll plot the different intervals using matplotlib for one timeseries. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "def _plot_fcst(fcst, train, model): \n", + " fig, ax = plt.subplots(1, 1, figsize = (20,7))\n", + " plt.plot(np.arange(0, len(train['y'])), train['y'])\n", + " plt.plot(np.arange(len(train['y']), len(train['y']) + 24), fcst[model], label=model)\n", + " plt.plot(np.arange(len(train['y']), len(train['y']) + 24), fcst[f'{model}-lo-90'], color = 'r', label='lo-90')\n", + " plt.plot(np.arange(len(train['y']), len(train['y']) + 24), fcst[f'{model}-hi-90'], color = 'r', label='hi-90')\n", + " plt.plot(np.arange(len(train['y']), len(train['y']) + 24), fcst[f'{model}-lo-80'], color = 'g', label='lo-80')\n", + " plt.plot(np.arange(len(train['y']), len(train['y']) + 24), fcst[f'{model}-hi-80'], color = 'g', label='hi-80')\n", + " plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "id = \"H105\"\n", + "temp_train = train.loc[train['unique_id'] == id]\n", + "temp_forecast = forecasts.loc[forecasts['unique_id'] == id]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The prediction interval with the SeasonalExponentialSmoothing seen below. Even if the model generates a point forecast, we are able to get a prediction interval. The 80% prediction interval does not cross the 90% prediction interval, which is a sign that the intervals are calibrated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_plot_fcst(temp_forecast, temp_train, \"SeasonalES\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For weaker fitting models, the conformal prediction interval can be larger. A better model corresponds to a narrower interval." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_plot_fcst(temp_forecast, temp_train,\"ADIDA\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ARIMA is an example of a model that provides a forecast distribution, but we can still use conformal prediction to generate the prediction interval. As mentioned earlier, this method has the benefit of not assuming normality." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_plot_fcst(temp_forecast, temp_train,\"ARIMA\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Future work\n", + "\n", + "Conformal prediction has become a powerful framework for uncertainty quantification, providing well-calibrated prediction intervals without making any distributional assumptions. Its use has surged in both academia and industry over the past few years. We'll continue working on it, and future tutorials may include:\n", + "\n", + "- Exploring larger datasets\n", + "- Incorporating industry-specific examples\n", + "- Investigating specialized methods like the jackknife+ that are closely related to conformal prediction (for details on the jackknife+ see [here](https://valeman.medium.com/jackknife-a-swiss-knife-of-conformal-prediction-for-regression-ce3b56432f4f)).\n", + "\n", + "If you're interested in any of these, or in any other related topic, please let us know by opening an issue on [GitHub](https://github.com/Nixtla/statsforecast/issues)\n", + "\n", + "## Acknowledgements\n", + "We would like to thank [Kevin Kho](https://github.com/kvnkho) for writing this tutorial, and Valeriy [Manokhin](https://github.com/valeman) for his expertise on conformal prediction, as well as for promoting this work." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "[Manokhin, Valery. (2022). Machine Learning for Probabilistic Prediction. 10.5281/zenodo.6727505. ](https://zenodo.org/record/6727505)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/nbs/docs/tutorials/ElectricityLoadForecasting.ipynb b/nbs/docs/tutorials/ElectricityLoadForecasting.ipynb index 73c4ab531..7e3d87aee 100644 --- a/nbs/docs/tutorials/ElectricityLoadForecasting.ipynb +++ b/nbs/docs/tutorials/ElectricityLoadForecasting.ipynb @@ -1,11 +1,32 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "f0139004", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "\n", + "# This is to render Plotly plots into HTML\n", + "# For more information, see https://quarto.org/docs/interactive/widgets/jupyter.html#plotly\n", + "import plotly.io as pio\n", + "pio.renderers.default = \"plotly_mimetype+notebook_connected\"\n", + "\n", + "import warnings\n", + "warnings.simplefilter('ignore')\n", + "\n", + "import logging\n", + "logging.getLogger('statsforecast').setLevel(logging.ERROR)" + ] + }, { "cell_type": "markdown", "id": "9c569427-7998-4bc2-95a6-244a14c08a6e", "metadata": {}, "source": [ - "# Electricity load forecast\n", + "# Electricity Load Forecast\n", "\n", "> In this example we will show how to perform electricity load forecasting considering a model capable of handling multiple seasonalities (MSTL)." ] @@ -51,7 +72,9 @@ "metadata": {}, "source": [ "In this example we will use the following libraries:\n", - "- [`StatsForecast`](https://github.com/Nixtla/statsforecast). Lightning ⚑️ fast forecasting with statistical and econometric models. Includes the MSTL model for multiple seasonalities.\n", + "\n", + "\n", + "- `StatsForecast`. Lightning ⚑️ fast forecasting with statistical and econometric models. Includes the MSTL model for multiple seasonalities.\n", "- [`DatasetsForecast`](https://github.com/Nixtla/datasetsforecast). Used to evaluate the performance of the forecasts.\n", "- [`Prophet`](https://github.com/facebook/prophet). Benchmark model developed by Facebook.\n", "- [`NeuralProphet`](https://github.com/ourownstory/neural_prophet). Deep Learning version of `Prophet`. Used as benchark." @@ -130,10 +153,7 @@ { "data": { "text/html": [ - "\n", - "
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" ], "text/plain": [ " unique_id ds y\n", @@ -282,7 +226,7 @@ } ], "source": [ - "df = pd.read_csv('https://raw.githubusercontent.com/jnagura/Energy-consumption-prediction-analysis/master/PJM_Load_hourly.csv')\n", + "df = pd.read_csv('https://raw.githubusercontent.com/panambY/Hourly_Energy_Consumption/master/data/PJM_Load_hourly.csv')\n", "df.columns = ['ds', 'y']\n", "df.insert(0, 'unique_id', 'PJM_Load_hourly')\n", "df['ds'] = pd.to_datetime(df['ds'])\n", @@ -299,7 +243,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": null, @@ -308,14 +252,12 @@ }, { "data": { - "image/png": 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", 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" ], "text/plain": [ " data trend seasonal24 seasonal168 remainder\n", @@ -712,14 +566,12 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ], "text/plain": [ " ds MSTL MSTL-lo-90 MSTL-hi-90 \\\n", "unique_id \n", - "PJM_Load_hourly 2001-12-31 01:00:00 28345.212891 27973.570312 28716.853516 \n", + "PJM_Load_hourly 2001-12-31 01:00:00 28345.212891 27973.572266 28716.853516 \n", "PJM_Load_hourly 2001-12-31 02:00:00 27567.455078 26917.085938 28217.824219 \n", "PJM_Load_hourly 2001-12-31 03:00:00 27260.001953 26372.138672 28147.865234 \n", "PJM_Load_hourly 2001-12-31 04:00:00 27328.125000 26236.410156 28419.839844 \n", @@ -1292,7 +984,7 @@ "\n", " SeasonalNaive SeasonalNaive-lo-90 SeasonalNaive-hi-90 \n", "unique_id \n", - "PJM_Load_hourly 28326.0 23468.693359 33183.308594 \n", + "PJM_Load_hourly 28326.0 23468.693359 33183.304688 \n", "PJM_Load_hourly 27362.0 22504.693359 32219.306641 \n", "PJM_Load_hourly 27108.0 22250.693359 31965.306641 \n", "PJM_Load_hourly 26865.0 22007.693359 31722.306641 \n", @@ -1322,7 +1014,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "MSTL Time: 0.43 minutes\n" + "MSTL Time: 0.22 minutes\n" ] } ], @@ -1347,14 +1039,12 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", 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" ], "text/plain": [ " model time (mins)\n", - "0 MSTL 0.425080\n", - "1 Prophet 0.928628" + "0 MSTL 0.217266\n", + "1 Prophet 0.301172" ] }, "execution_count": null, @@ -2007,14 +1451,12 @@ "outputs": [ { "data": { - "image/png": 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", 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" ], "text/plain": [ " unique_id ds NeuralProphet NeuralProphet-lo-90 \\\n", - "0 PJM_Load_hourly 2001-12-31 01:00:00 25015.931641 22304.785156 \n", - "1 PJM_Load_hourly 2001-12-31 02:00:00 24125.677734 21462.275391 \n", - "2 PJM_Load_hourly 2001-12-31 03:00:00 23730.714844 20973.000000 \n", - "3 PJM_Load_hourly 2001-12-31 04:00:00 23469.591797 20735.953125 \n", - "4 PJM_Load_hourly 2001-12-31 05:00:00 23887.208984 21221.152344 \n", + "0 PJM_Load_hourly 2001-12-31 01:00:00 25019.892578 22296.675781 \n", + "1 PJM_Load_hourly 2001-12-31 02:00:00 24128.816406 21439.851562 \n", + "2 PJM_Load_hourly 2001-12-31 03:00:00 23736.679688 20961.978516 \n", + "3 PJM_Load_hourly 2001-12-31 04:00:00 23476.744141 20731.619141 \n", + "4 PJM_Load_hourly 2001-12-31 05:00:00 23899.162109 21217.503906 \n", "\n", " NeuralProphet-hi-90 \n", - "0 27442.351562 \n", - "1 26571.785156 \n", - "2 26321.234375 \n", - "3 26097.132812 \n", - "4 26459.939453 " + "0 27408.724609 \n", + "1 26551.615234 \n", + "2 26289.349609 \n", + "3 26050.443359 \n", + "4 26449.603516 " ] }, "execution_count": null, @@ -2507,7 +1774,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Prophet Time: 2.39 minutes\n" + "Prophet Time: 2.95 minutes\n" ] } ], @@ -2525,10 +1792,7 @@ { "data": { "text/html": [ - "\n", - "
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" ], "text/plain": [ " model time (mins)\n", - "0 MSTL 0.425080\n", - "1 Prophet 0.928628\n", - "2 NeuralProphet 2.393841" + "0 MSTL 0.217266\n", + "1 Prophet 0.301172\n", + "2 NeuralProphet 2.946358" ] }, "execution_count": null, @@ -2689,14 +1877,12 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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Njrv33nvDwZ2MjAyuvvrqBmPOOOMMHA6rre7jjz/eaFAuJyeHyZMnEwgEIvUS9tlvfvMbwPo3NHv2bMB6bRdccEE0tyUiIiIi0iJHtDcgIiIiIiLSVp999lmrgwNTp05l9OjRjBs3jr/85S/cd999eL1ezjnnHKZMmcKZZ55JYmIiGzduZPbs2eGATEZGBk8//XSD+SZNmsSgQYPYsWMH3377LSNHjmTmzJkMHz6c6upqvvjiC1555RVqamqYPn06zz33XJN7S05O5oknnuDiiy/GNE1uuukm3n77bSZPnkxaWhrbtm1jzpw5rF+/nkmTJjFv3rx9+np1loSEBJ577jkmTpxIMBjkzjvv5KOPPuKSSy6hb9++5OXl8corr4T7+DgcDp577jkSExMbzJWZmcmVV17J7NmzKSsr45hjjuH666/nkEMOwefzsWLFCp577jmqq6u55JJLePXVVzv75dZz/vnn069fP3Jzc8PPzZgxA6fTGcVdiYiIiIi0TIEiEREREZH9UN1MF7vdHsWd7JuvvvqKr776qlVjjzrqKEaPHg1YmSwul4t77rmHQCDA66+/zuuvv97gnDFjxvDuu+/Sr1+/Bsfcbjfz5s3j7LPPpqioiB07dnDnnXfWG2O323nggQcYN25cs4EisMquPfnkk9x44434/X4WLlzYoB/RSSedxOzZs7t8oAjgrLPO4p133mHatGmUlJSwdOnScGCorl69evHCCy9w1llnNTnXww8/zJo1a1i2bBklJSX8/e9/r3c8NjaWZ599lmAwGPVAkcPh4JprruHuu+8GrJ5NXbkco4iIiIhILZWeExERERHZD3k8nvDjhISEKO6k891xxx2sWbOGm2++mYMPPpjk5GRcLhf9+vVj4sSJPPvss6xcuZJhw4Y1OceRRx7JqlWruPXWWxk5ciQxMTEkJCRw4IEH8utf/5rly5fzpz/9qdV7uuaaa/jxxx+ZOXMmgwcPxu1207t3b0444QSefPJJFi5cSHJyciRefqeYOHEiW7du5e9//zvjx48nPT0dh8NBeno6xx9/PH/729/YunUrEydObHae5ORkvvjiCx599FHGjRtHUlISMTExDBs2jN/85jf88MMPTJs2rZNeVcvOPPPMeo+HDh0axd2IiIiIiLSOYbZUNFtERERERHqcE044ga+//hqAr7/+mvHjx0d5RyLd3+9//3see+wxAObNm8dFF10U5R2JiIiIiLRMgSIRERERkf2M3+8nPT2diooKDMOgpKSkW2WriHRFVVVVDBw4kJKSEvr3709WVhYOh6q9i4iIiEjXp9JzIiIiIiL7mX/9619UVFQAMGHCBAWJRCLg4YcfpqSkBIAbb7xRQSIRERER6TaUUSQiIiIi0sOVlZXx+eefU1payoIFC3j55ZepfRvw/vvvt9gnRkQaysnJYfXq1Xg8Hr744gv+/e9/EwwGycjIYPPmzftd7y8RERER6b50i5OIiIiISA+3bdu2Rnul3HnnnQoSieyjBQsWcPXVV9d7zm63M2vWLAWJRERERKRbUaBIRERERGQ/ERMTQ2ZmJuPHj+f6669n/Pjx0d6SSI+QkZHBEUccwR133MFxxx0X7e2IiIiIiLSJSs+JiIiIiIiIiIiIiIjsp5RRFGWhUIjc3FwSExMxDCPa2xERERERERERERERkSgyTZOKigr69euHzWbr8PUUKIqy3NxcBg4cGO1tiIiIiIiIiIiIiIhIF5Kdnc2AAQM6fB0FiqIsMTERsBoMp6amRnk3IiIiIiIiIiIiIiISTbt372bo0KHh+EFHU6AoymrLzSUmJpKUlBTl3YiIiIiIiIiIiIiISDTV1NQAdFq7mo4vbiciIiIiIiIiIiIiIiJdkgJFIiIiIiIiIiIiIiIi+ykFikRERERERERERERERPZTChSJiIiIiIiIiIiIiIjspxQoEhERERERERERERER2U8pUCQiIiIiIiIiIiIiIrKfUqBIRERERERERERERERkP+WI9gZk35imSU1NDaFQKNpbERGJCLvdjtPpjPY2RERERERERERE9isKFHUzfr+fgoICqqurCQaD0d6OiEhEud1u0tPTSUpKivZWRERERERERERE9gsKFHUj1dXVZGdnY7fb6dWrF7GxsdjtdgzDiPbWRETapTZLsqysjJycHAAFi0RERERERERERDqBAkXdSFFREU6nk8GDB2O326O9HRGRiIqNjSUxMZGdO3dSVFSkQJGIiIiIiIiIiEgnsEV7A9I6gUCAqqoqUlNTFSQSkR7LMAySk5Px+XzU1NREezsiIiIiIiIiIiI9ngJF3UQgEACs/h0iIj2Z0+kEUB82ERERERERERGRTqBAUTejfkQi0tPp/zkREREREREREZHOo0CRiIiIiIiIiIiIiIjIfkqBIhERERERERERERERkf2UAkUiIiIiIiIiIiIiIiL7KQWKRERERERERERERERE9lMKFImIiIiIiIiIiIiIiOynFCgSERERERERERERERHZTylQJN3Wt99+i2EYHH/88U2O+fvf/45hGPz1r3/txJ2JiIiIiIiIiIiIiHQPChRJt3XUUUdxxBFHsHTpUtasWdPguGmazJo1C5vNxq9+9aso7FBEREREREREREREoibgAX9JtHfR5SlQJN3addddB8DTTz/d4NjChQvZunUrZ511FoMGDersrYmIiIiIiIiIiIhItAQ8sPMdyHoJqnZEezddmgJF0q1dfvnlJCUl8cILL+Dz+eode+aZZwC45pprorE1EREREREREREREYmGUA3kLYCy1eAtgp1vQ+XWaO+qy3JEewMSOb/419cUVvhaHtgF9E50895NE9o9T3x8PFdccQX/+c9/ePPNN7n88ssBKCoq4q233iIzM5Nf/OIX7V5HRERERERERERERLoBMwT5i6B4BcQfAPZYK0iU/Q70Pw+SRkR7h12OAkU9SGGFj7xyb7S30emuu+46/vOf//D000+HA0XPP/88fr+fq6++GodD/8xFREREREREREREejzThKJvoHAxxPYHR5z1fOIwqNwGO9+F/udC8ujo7rOL0RX0HqR3ojvaW2i1SO714IMP5vjjj2fRokVs2rSJESNGMGvWLAzDYObMmRFbR0RERERERERERES6sNKVkLcQ3GngSq5/LGGo1aso5z0wA5ByMIRCkJ8P5eUwYgTY9s9uPQoU9SCRKOXWXV133XUsWbKEZ555hvPPP5+1a9dy+umnc8ABB0R7ayIiIiIiIiIiIiLS0co3wq5PrCwid3rjY+IHQfl2+HYu1IyCXaYVKAoG4YwzYNy4zt1zF6FAkfQIF198MTfffDNz5sxh586dAFxzzTVR3pWIiIiIiIiIiIiIdLiqbMj9EEJBSBjY8LivBvJ3Q24xbM2F/FzwLofMsTDgEKiqgs8+g8REGDOm8/cfZQoUSY8QExPD9OnTefjhh3nppZfo3bs3F154YbS3JSIiIiIiIiIiIiIdyVsAOe+DvxwSh9c/VlACG7JhWx6UVEAwBIlxMGAQ2DwQyINQOvQZAdnZMH8+JCTAoEHReS1Rsn8W3JMe6de//jWGYQAwffp0XC5XlHckIiIiIiIiIiIiIh3GX2ZlEnl2QeIw2HN9GIDNOfD+N7BsPdQEYEBvGN4fMnqB2wXOZKtMXelqKN8AAwdCRQV88AEUFkbvNUWBAkXSYxx44IEMGDAAgJkzZ0Z5NyIiIiIiIiIiIiLSYQLVkPsRVGyGxBFg7Al3BEPw3UaYv8IqOTeiP/ROAWcjBdacSeBIgLK1UJ0Nw4bBrl3w0UdW0Gg/oUCR9BhLly4lOzubk046iZEjR0Z7OyIiIiIiIiIiIiLSUQq+srKBEkaAbU8QyOuHL1fCl6sgPgb6p9fPMmqMMxFsTihbA4FyGDECNm6ETz4Bn6/jX0cXoECR9Bh/+9vfALjxxhujvBMRERERERERERER6VDV2eDqBfY9LUhKK60sou82QWYqpCa1fi5XKgSqrMCTEbIyi374AT7/HEKhjtl/F9JIrpVI97FkyRJmzZrFTz/9xPLlyzniiCOYNGlStLclIiIiIiIiIiIiIh0l6LcCO/YY6/OdhVYW0a7dMCQTXG0MfRgGuPuAJxfK10HKIVbPoiVLIDERxo+P/GvoQhQokm5t48aNzJ49m8TERCZOnMjjjz+OzaZEOREREREREREREZEeK1gFIR84kmHdDvh6NXi8MKwv7Ov1YZsD3OlWzyNXCiQNBr/fyipKTIRDDonoS+hKFCiSbu2qq67iqquuivY2RERERERERERERKSzBKrAWw0bSmDFZohxwZC+7Z/XHgs2D5SuAUcSpKdbfYo++QQSEuCAA9q/Rhek1AsREREREREREREREek+vOWweD0s2QApCVZPokhx9YJgtdWvKOiD/v3B64WPPoLi4sit04UoUCQiIiIiIiIiIiIiIt2DacLiJbA6B/qnWYGiSDIMcGeAZ5fVr8gMWZlEeXmwdWtk1+oiun2g6IEHHsAwDH7/+9+Hn/N6vdxwww2kpaWRkJDA5MmTyc/Pr3fejh07mDhxInFxcfTp04fbbruNQCBQb8yiRYs44ogjcLvdDB8+nDlz5jRY//HHH2fIkCHExMQwbtw4li9f3hEvU0REREREREREREREVq2Cr7+BtASIi+mYNWx2iEmHii1Qtd0KHsXFwYYNVqCqh+nWgaIVK1bw5JNPcsjPmkjdfPPNvPfee7z++ut88cUX5ObmMmnSpPDxYDDIxIkT8fv9LFmyhOeee445c+Zw5513hsds27aNiRMncsopp/Djjz/y+9//npkzZzJ//vzwmFdffZVbbrmFv/71r3z//fcceuihnHXWWRQUFHT8ixcRERERERERERER2Z9s2wYLFoDLjHwm0c/ZY8EeA2VrwVcMqamQm9sjy89120BRZWUlU6dO5emnn6ZXr17h58vKypg1axYPP/wwp556KkceeSTPPvssS5Ys4ZtvvgHgk08+Ye3atcydO5fDDjuMc845h3vvvZfHH38cv98PwH//+1+GDh3KQw89xOjRo7nxxhuZMmUKjzzySHithx9+mGuuuYarr76aMWPG8N///pe4uDhmz57duV8MEREREREREREREZGerLDQ6hPk9UJ6DNgcHb+mqxcEvVa/olgnVFRAdnbHr9vJum2g6IYbbmDixImcfvrp9Z7/7rvvqKmpqff8qFGjGDRoEEuXLgVg6dKlHHzwwWRkZITHnHXWWZSXl7NmzZrwmJ/PfdZZZ4Xn8Pv9fPfdd/XG2Gw2Tj/99PAYERERERERERERERFpp8pK+Phjq0/QkMEQrAKbs3PWdvcBb4HVr8jlhM2bO2fdTtQJIbfIe+WVV/j+++9ZsWJFg2N5eXm4XC5SUlLqPZ+RkUFeXl54TN0gUe3x2mPNjSkvL8fj8VBSUkIwGGx0zPr165vcu8/nw+fzhT8vLy8HoKamhpqamibPq6mpwTRNQqEQoVCoyXEiIt1dKBTCNE1qamqw2+3R3o6IiIiIiIiIiERTTQ18+ils3AgjRoAZsJ63xYDRCdeO7HaIyQRPDsQfaGUUFRdDUlKHLdlcrKAjdLtAUXZ2Nr/73e9YsGABMTEd1KiqA91///3cfffdDZ7//PPPiYuLa/I8h8NBZmYmlZWV4fJ4IiI9kd/vx+Px8OWXXxIIBKK9HRERERERERERiTbDgKOO2vt50kXR2wvA11936PTV1dUdOv/PdbtA0XfffUdBQQFHHHFE+LlgMMiXX37Jv//9b+bPn4/f76e0tLReVlF+fj6ZmZkAZGZmsnz58nrz5ufnh4/V/ln7XN0xSUlJxMbGYrfbsdvtjY6pnaMxf/7zn7nlllvCn5eXlzNw4EBOOeUU0tLSmjzP6/WSnZ1NQkJCtwyQiYi0ltfrJTY2lhNPPFH/34mIiIiIiIiI7M9WrrRKzqWnQ3Ky9Zy3EIqWWCXhjE7srhP0WiXvyvrDUcfDOed02FLFxcUdNndjul2g6LTTTmP16tX1nrv66qsZNWoUf/rTnxg4cCBOp5OFCxcyefJkADZs2MCOHTs47rjjADjuuOP429/+RkFBAX369AFgwYIFJCUlMWbMmPCYDz/8sN46CxYsCM/hcrk48sgjWbhwIRdeeCFglUtauHAhN954Y5P7d7vduN3uBs87nU6czqZrKgaDQQzDwGazYbN129ZSEWcYRvjxkiVLwn8/P/faa69xySWXADB48GCysrIajJk3bx7PPPMM3377LSUlJSQnJ5ORkcERRxzBSSedxJVXXonL5WLIkCFs3769Tfs0TbPefms/F5GGbDYbhmG0+P+iiIiIiIiIiIj0YJs3w+efQ1zc3iARWAGbkB8wwQx23n5sTvBWgrsatm4Fvx/i4ztkqc6+JtbtAkWJiYmMHTu23nPx8fGkpaWFn//Vr37FLbfcQmpqKklJSdx0000cd9xxHHvssQCceeaZjBkzhmnTpvHggw+Sl5fH//7v/3LDDTeEgzjXXXcd//73v/njH//IjBkz+Oyzz3jttdf44IMPwuvecsstTJ8+naOOOopjjjmGRx99lKqqKq6++upO+mpIXS+++GKTgaK5c+c2e+6vfvUrZs+eDcDhhx/OSSedhGmarFmzhrlz5zJ37lzOO+88MjMzmTJlCkVFRfXO//HHH1m5ciXDhg1jwoQJkXlBIiIiIiIiIiIiIj2Jzwc5OVbfH5cLnE7ro/axY0/IIj8f5s+3gjEHHFB/jpCv8/ddyxEPtlIoLoCdO2HkyOjtJYK6XaCoNR555BFsNhuTJ0/G5/Nx1lln8Z///Cd83G638/7773P99ddz3HHHER8fz/Tp07nnnnvCY4YOHcoHH3zAzTffzGOPPcaAAQN45plnOOuss8JjLrnkEgoLC7nzzjvJy8vjsMMO4+OPPyYjI6NTX+/+zm63M2bMGF599VUeffRRHI76/6yLi4v5+OOPOeKII/j+++8bnD9v3jxmz55NcnIyH374Iccff3y941lZWcyaNSscRPznP//ZYI677rqLlStXMmHCBObMmRO5FyciIiIiIiIiIiLSE1RWWmXkfvrJ6jnkcFgBI7t972OnE2JiwOu1gkWjRjWcJ+gFjIbPdwZHEvjywLcbtm9XoKgrWbRoUb3PY2JiePzxx3n88cebPGfw4MENSsv93Mknn8wPP/zQ7Jgbb7yx2VJz0jmmTp3K7bffzvz585k4cWK9Y6+++io1NTVcccUVjQaK3nzzTcD6u/x5kAhgyJAh3HvvvR2zcREREREREREREZGerqQEPvgA1q2D4cOtwFAwaH0EAnv/9HqtgBLAiBFWQOnnApVgRCm0YbMDDoipgg0b4KSToJFWM92Nmt1Ij3D55ZdjGEajJebmzp1LQkICF1xwQaPnFhYWAtC7d+8O3aOIiIiIiIiIiIjIfic/H956ywqsjBxpZQw5HFaAJS4OkpKgVy/o3RsyM2HAAOvD0UQwKFBl9QuKFley1acob5tVRq8HUKBIeoSBAwdy4okn8u6771JZG3EGtm7dytKlS7nooouIi4tr8lyA559/vt65IiIiIiIiIiIiItIO2dkwbx5kZVlBImc7AzyhGgj5o5dRBGCPAXsIqgtgx47o7SOCFCiSHuOKK66gurqaefPmhZ978cUXw8eaMmPGDGw2G99//z0HHHAA119/PS+88AJr167FNM0O37eIiIiIiIiIiIhIj7NpE7z5JhQWWkEiu739c4b8EApEN6MIwJkErgpY86NVMq+bU6BIeowpU6bgdrvDwSGwAkV9+/bltNNOa/K88ePHM3fuXNLS0igsLOS///0vV155JQcddBB9+/blz3/+M2VlZZ3xEkRERERERERERES6v9Wr4e23obra6klki1AoIui1soqimVEE4EiAuBDkbIRdu6K7lwiI8ldTIurJk6CyINq7aJ2EPvDrLyI6ZUpKChMnTuSdd94hLy+P7OxsNmzYwM0334y9hWj1ZZddxvnnn8/bb7/NwoULWbFiBWvWrCE/P58HHniAN954g8WLF9OnT5+I7llERERERERERESkxzBN+PZbWLDAKjM3dGhk5w/5gSDYohzaMGwQFw+FO2HHdtjT3qS7UqCoJ6ksgIrcaO8iqq644grmzZvHK6+8wrZt28LPtUZ8fDxTp05l6tSpAOTn5/Pss89yzz33sHnzZv7nf/6Hp59+usP2LiIiIiIiIiIiItJtBYOweDEsWgRJSZCR0QFr+CI/575yJoNzN6xcDMcdH7msqShQoKgnSehG2S4dtNdzzz2XlJQUnn/+eXJzcxk9ejRHHHHEPs2VkZHB7bffTlxcHL/73e/44IMPIrxbERERERERERERkW7C5wOPxyonV/fPykooK4OSEti+Hfr0gdTUjtlDyAd72sqHzBA2I4rBGZsLktyQvQ4KCiAzM3p7aScFinqSCJdy647cbjcXX3xxOPPnt7/9bbvnPPXUUwEoKipq91wiIiIiIiIiIiIi3cquXVaWUH4+1NSA328FjWoZBrhc1seAAZCQELGlTdPEF/ThqfHgDXjx7N5CVWURFZVVVNR46BvbiwOTM4mxuyK2ZpskpcGmHNi6RoEika5k2rRpzJs3D8MwwmXkmmOaJoZhNHl88+bNAPTv3z9iexQRERERERERERHp0oJBWLnSChKVlUHv3hAbC2631X+og0qteQNetpdup9RbRoW/HF/Ahz/oJxAKQNV2jJAfpzOAw7CxviyHYn8FY5IHkBGb3CH7aZYjAZwBWLUYjjvVCpp1QwoUSY9zwgkntCn7Z+bMmQwdOpQZM2bQr1+/esc2bNjArbfeCsCUKVMiuk8RERERERERERGRLqmsDL74Ar7/HhITYeTITgmClHhKWJW/iryqPFw2Fy679RHvisdpc2AEi8CMA0ccAEnOWIp85XxTuIkRSZkMT8rEZevEsIdhQGov2LoSCnZBRr+Wz+mCFCiS/V5xcTGzZ8/mr3/9KwcddBAHHnggdrud7du3s2LFCkKhEMcccwx33HFHxNY89thjmzw2c+ZMZs6cGbG1RERERERERERERFpt82b47DPYsQOGDIH4+A5f0jRNssuz+Sn/J6prqumX0A+7Ya8/KOQHMwTG3rCG3WYjIzaFihovP5XspNhXyZiU/qS5Ezt8z2EpfWDXWti0AjIu6Lx1I0iBItnvPf7445x33nnMnz+ftWvXsnDhQiorK+nVqxennHIKF198MTNmzMDpdEZszWXLljV57Oyzz47YOiIiIiIiIiIiIiKt4vPBN9/AkiVW2bnRozusvFxd/qCfDUUb2Lh7Ey6bk76JfTFoJHspFAAzCDZ3g0OJzhhi7U7yvWWUFlQxMqkfByT2wWGzN5wn0uxOcDpg9VcwoXsGigzTNM1ob2J/Vl5eTnJyMkVFRaSlpTU5zuv1sm3bNoYOHUpMTEwn7lBEpHPp/zsRERERERERkU6Wnw8LF8LatZCZCc1cq46kcm85qwtWk1ORQ2pMKnHOuKYH15RB+XpwpUBjgaQ9yvweKgIeBsSlcVBKf5JdzcwZKcWF4KmAWx+DjOHtn664mPT0dMrKykhKSorABpunjCIRERERERERERERkf1RKASrV8Pnn8Pu3TBiBLhcnbJ0TkUuP+WvpsxXRmZ8Jo6WeguZAcCkuSARQLIrlliHi5zqYkr9lYxNGcighPSI7btRvdIgLwc2LI1IoKizdXzemIiIiIiIiIiIiIiIdC1eL3z6Kbz9NgQCMGpUpwSJAqEA6wrXsXznMrwBL/0T+7ccJAII1bR6DZfNTr/YXgRDJj/sziKnanc7dtwKNhs442DtYgh4OnatDqCMIhERERERERERERGR/cnu3fDJJ/DTTzBwIHRCeTOAan81qwt+YntZFsnuZBJdia0/OeSnpWyiugzDIC0mgSJvBStLtuO2O0mPacN6bZXaB7ZmQcEa6HdUx63TAZRRJCIiIiIiIiIiIiKyv8jKgtdfhzVrYPjwTgsSlXnLWJ67nO1lWfSJ79O2IBFAyAeGvc3rpsck4gnW8OPuLMprOjDbp1ciVHhhyw8dt0YHUaBIRERERERERERERKQ7mT8f3nvPCvqEQq07xzTh+++tIFFBgVVqzu3u0G3WKqouYnnOcgqrCumb0BeXbR9K3AX3LVAEkBGTTIm/ih+Ls/AE/fs0R4vsdjBcsG0NhIIds0YHUek5EREREREREREREZHuoqAAVq6E0lLrz2HD4LDDrD+b6jHk88GXX8KSJZCYaGUSdZKcilxW5v2IJ+Clb2JfbPuSv2IGIBQAY99yX2yGQWZMCrs8JazavYMj0obgbE1fpLaKT4ScHPAXQ0yfyM/fQRQoEhEREZH9Xll1DUmxDgyj9fWuRUREREREomLdOigvh4MPhupq2LwZNmyAAQPg8MNh5EhISNg7vqTE6ke0erU1Jjm5U7ZpmiZZpVmsLliNaZr0jc9sx2QBIAhGzD5PYbfZ6BObzPbKQtx2B4ekDMJmi3DRtYREKM6F4u3QX4EiEREREZEuz1sT5C9vrWbe9zmcPjqDp688UsEiERERERHpuqqqYNUqSEuzPo+Ls7KD/H7Iy4O334Y+fawMo9GjrfHz50N2tpVxFLPvgZa2CIVCbCjewLqidbjtbnrF9mrnhDVWOTd7+wI7LpuDdHcSm8rzcNucjEruF9n3gAmxUOCHXZuh/9GRm7eDKVAkIiIiIvulgnIv17zwHSuzSwH4dF0+izYUcsqo7nPXl4iIiIiI7Gc2bbJKz40cWf95lwsGDbL6FeXnW8GhZcusY9XVVj+iSGfPNCEQCrCmYA0bizeR5E4k0ZXY/klDASC0zz2K6opxOEky41hXlkOs3cWQxN7t318tpwNCDti1weoJ1U1uROycfxkiIiIiIl3ITzllnP/vxeEgUa1/frIB0zSjsykREREREZHmBALw449WFpG9iYCJzQZ9+8KYMRAbC243jBjRaUEib8DL97u+Z0PxBnrF9opMkAj2lJ6LnERnDG6bk9UlO9jlKYno3DjckJcDNWWRnbcDKVAkIiIiIvuVD1btYsp/l5BX7gWgX3IMI/pY9bvX5JYzf01eNLcnIiIiIiLSuKws2LEDMlvR68cwIDUV0tM7fFu1Kv1VfJv7LVmlWfSJ60OcIzZyk4dqIjfXHr3c8QQxWVm8g92+yshNnJAEufngKYjcnB1MgSIRERER2S+Ypsmjn27khpe+x1sTAuCIQSm8c+ME/nLu6PC4hxdsJBhSVpGIiIiIiHQxq1dbpeU6qc9QWxRVF7Fs5zfkVuSSGZ+Jy+6K7AIhLx0RzujtTqQy6OXH3dupDvgiM2l8LJR7oHh7ZObrBAoUiYiIiEiP5/EHufHlH3j0003h5yYd0Z+Xrz2W3oluTh7ZmyMGpQCwMb+S91flRmmnIiIiIiIijcjLg40bW5dN1IlM02R76XaW7VxGqa+Mfon9cNgckV8o6ItIf6KfMwyDzJgUirwVrCndSSgUav+kcTHgNSFnTfvn6iQKFImIiIhIj7arzMPFTy7hg1W7AKsCw1/OHcVDFx+K22Hf85zBH87c2wz20U83EQhG4A2CiIiIiIhIJKxfD5WVkJwc7Z2EBUIB1hSs4dtd32Fi0jc+E1uHhBxCEPJ3SKAIwGYYpMckklVVRFZVYfsndNjBdEDBDghUtX++TqBAkXRrhmFgGAYpKSmUlpY2OuaBBx7AMAzuuuuuTt1bR8rKysIwDE4++eR6zy9atCj8Nan9cDgcZGZmcsEFF/D5559HZ8MtaOr1iIiItNfK7FIu+PdifsopByDeZeeZK4/i2hOHYRhGvbHHD0/nuAPSANhWVMW8H3I6fb8iIiIiIiINVFbCypWQlhbtnYRV+6v5Lvd71hatJcmVSGpMasctFgqAGeqwQBFAjN1JvN3FurIcin0V7Z/QFWtlgXkjEHjqBAoUSY9QVlbGww8/HO1tdBkZGRlMnz6d6dOnM2XKFFJSUnj33Xc57bTTeOKJJ6K9vahRQEpEZP9SEwzxmxe/p6DCqjM9MDWWeb8Zz2mjM5o859YzDww/fuzTTfgDyioSEREREZEo27gRioqgd+9o7wSAYk8xy3KWsb0siz7xfYh3xnfsgqEAmMEODRQBpLji8QYD/FSyE2/Q377J4uNhVxl48iOzuQ6mQJF0e4ZhEBMTw2OPPUZJSUm0t9MljBo1ijlz5jBnzhxeeeUV1q1bx1133YVpmtx6660UFBREe4siIiIdbmeJh5xSDwCjMhN554YJjMxMbPaco4akctKB1puvnFIPr36b3eH7FBERERERaVJNDfz4oxV4sEX3cr5pmuwo28GyncvY7dlNv4R+uGyuTli4xgoU2To2UATQx51EnqeUDWW5mKa57xPFx0B1API2RG5zHUiBIun2bDYb1157LeXl5fzzn/+M9na6JMMwuOOOOxg2bBgej4dPPvkk2lsSERHpcLl7gkQAJx3Ym9T41r2BqZtV9O/PNuGtCUZ8byIiIiIiIq2ybRtkZ0NmZlS3EQgFWFe0nu9yvyMYCtI3oS82o5PCC2YAMAGjpZHtZrfZSHMnsrmigOyq4n2fKM4NPgN2bYL2Zid1AgWKpEe4/fbbiY2N5V//+hfFxa3/BjZNk5dffplTTz2VXr16ERMTw+jRo7nrrruorq5uMH7IkCEN+hnUqu0PdNVVV9V7/qqrrsIwDBYtWsT8+fM55ZRTSElJwTCMcF+lr776ihtvvJFDDjmEXr16ERsby6hRo7j99tub7L3UVjabjUMPPRSA7OzsBnvOy8tj5syZDBgwAIfDwaOPPho+d+nSpVxwwQX07t0bt9vNkCFD+M1vfkNubm6DdebMmRPuCbVhwwYmT55MWloa8fHxjB8/ng8//LDZfXo8Hm6//XYGDx6M2+1m+PDh/OMf/2gygr97927+/Oc/M2bMGGJjY0lOTubUU0/l/fffrzfurrvuYujQoQB88cUX9fo4/fzvTEREeoacOoGifimxrT7vkAEpnDnGKk+XX+5j7jfbI743ERERERGRFpkmrF5tPXa7o7aNmmANP+T9yE8Fq4l3xpMW28m9kkI1+3RaulHEIfZVDDB2YgWaWifO4cJl2FlTupMyf8NrxK1is4HhguIi8BXt2xydSIEi6RH69u3LddddR0VFBf/3f//XqnNCoRBTp07l8ssvZ8WKFRx22GGce+65VFVVcffdd3PKKafg8XhanqiVXnrpJc455xyqqqo455xzOProo8NBp9tuu41Zs2YRGxvLaaedxmmnnUZ5eTn/+Mc/mDBhApWVlRHZQ0WF1YjN/bMfLIWFhRx99NF88MEHHHfccZxzzjnExcUBMHfuXE444QTeffddRo4cyaRJk3C73TzxxBMcccQRrF+/vtG1tmzZwrhx4/jhhx8488wzOeqoo1i6dCnnnXcezz77bKPn+P1+zjzzTJ5++mmOOuooTjnlFHJycrj99tu54447GozfuHEjhx12GA888AAej4ezzjqLo446imXLlvGLX/yiXobZYYcdxuTJk4H6PZymT5/OhAkT2v7FFBGRLm9XqTf8uC2BIoCbz9ibVfTEoi1U+QIR25eIiIiIiEir5OXBpk1RzSYKhUKsKVzDtpKt9I7rTYIrIQqb8NPWbKK+xi4OtG8mwahmkH0nA4ycNp2f6k6gMuDlp9JsakL7+H7QHQN5ReAr3LfzO5ECRdJj/OlPfyIuLo5///vfFBa2/M330EMP8fLLL3PyySezadMmPv/8c+bNm8fmzZv51a9+xfLly7n77rsjtr+nn36al19+meXLl4f/TE5OBuCvf/0reXl5LFu2jDfeeIP333+fbdu2ce2117JmzRoefvjhdq9fUFDAsmXLADjkkEPqHfvwww85+uij2bZtG6+//jrvvfce1157LdnZ2Vx77bUAvPPOO3z99de8/PLLrFu3jt///vfk5+czbdq0RtebO3cuF1xwARs3buTll1/miy++4N1338Vms3HjjTeSk9PwP+elS5dit9vZtm0bb775Jh9//DFfffUVdrudRx55pF7ALBgMMmXKFLKzs3nwwQfZsmULb7/9NgsXLmTlypUMHTqU22+/nZ9++gmACy+8MBw4qtvDac6cOcycObPdX18REel6cutlFMW06dzRfZM475C+ABRX+XluaVYktyYiIiIiItKytWuhqgqSkqKyvGmarC9ez6biTaTHpuO2RymrKegDo7X9iUwG2rIZaq9fGWKQfSe9jdYHbAzDICMmmZyqEjaW5bVhs3XEx0BhNVTs3LfzO5ECRdJjZGRkcP3111NVVcU//vGPZscGAgEefPBB4uPjeeWVV8isE5V3uVz861//IjMzk6eeeopQKBSR/U2cOJFLLrmk0WPnnHNOOGhUy+128+ijj+JwOHjnnXf2eV2v18uyZcs4//zzKS8vZ+TIkZxyyikN1vrXv/5FTEz9i2jPPPMMHo+HX/7yl5x//vnh5202Gw888AD9+vXj22+/ZfHixQ3WTUhICO+/1nnnnceUKVOorq5uNKvIZrPx5JNPklTnh99RRx3FOeecQ3V1Nd9++234+ffee4/Vq1czefJkbrvtNmx1mvkNHz6chx56iGAwyNNPP92Gr5aIiPQkuWV7A0X925hRBPD70w/EtuemtSe/2Eq5d9/KHYiIiIiIiLRZeblVdi49PWpb2FKyhXWF60h2pxDjaNvNdxEVam2gyGSoLYuBtr03qJeG9l5zHWbbSrJR2uplHTY7Ka44NpbvIre6pA0b3iPODT5g11owI3ONuaM4Wh4i3cUl719Ckafr1zsESI9N59XzXo34vH/605/473//yxNPPMFtt91GRkZGo+O+//57ioqKOOOMMxodExsby5FHHskHH3zApk2bGDlyZLv3VjfQ0picnBzee+891q9fT3l5eThA5XK52LRpU5vWqu3B83PDhw/n7bffxm6v/x/rEUccQf/+/RuM/+qrrwCYOnVqg2Nut5uLL76Yxx57jK+++orx48fXO37mmWfSq1evBudddtllvPrqq+G56xo8eHCjX+sDD7TK/+zatSv83CeffALApEmTGowHOOGEEwBYvnx5o8dFRKTnq+1RFOeykxzrbPP5w/skcNHhA3jz+52UeWqY9dW2eiXpREREREREOszGjVBcDKNGRWX5HWU7WJ2/mjhnHAmu+KjsAQAzAKEAGM3nvBiEGGbbSh/b3uvjW4ODyTMzGUoWfW352AyTkbZN/BQcQzWte00Jzhiqg35+Ks0myRlLgrMNAbMYN/gMKMwH326IiV7QryUKFPUgRZ4iCqoLor2NqOrduzc33HADDz74IA888ACPPPJIo+OysrIAWLBgQaMBlbqKiooiEigaNGhQk8cefvhhbr/9dmpqInOnckZGBmeffTYADoeDtLQ0jj32WM477zyczoYXypraW25uLgBDhgxp9Hjt842VkRs8eHCz59TOXdeAAQMaPScxMREAn88Xfq7273Dq1KmNBrJqFRV1j+CpiIhElmma4dJz/VJiW/x535TfnTaCd37MIRAymfX1Nq46fgi94l2R3KqIiIiIiEh9NTXwww+QkAC2zi8KlleZx6r8VThsDpLdyS2f0JHMABAEo+kAjUGIkbZNpNqsrB/ThM2hYRSavQHYFhqCCz9pthIcRpDR9vWsDo7FT+tK6aW7E8n1lLCmdCdHpg3FYWtlGTybAYYTdhdbfYoUKJLOkB7bdf+h/VxH7vW2227jP//5D//973/54x//2OiY2myd4cOHN8iE+bm0tLRWrdtSibqfl3Wr9c0333DrrbeSnJzMY489xsknn0xmZiZut/UfVb9+/epl0rRGbQ+e1mpqby3Z14tuTbG14Qdf7df77LPPbjJzDCA9ium5IiISPSXVNXhrrJ8V/fah7FytQWlxXHzUQF5evoNKX4Anv9zK7edE544+ERERERHZT2zdCjk5MHRopy9d7Cnmx7yV1ARryIhv+ppbpwnVQCgI9savG9oIMsq2gRRbuTXcNNgYGsFuM7XOKINNoeG4jHUkGpW4jRrG2NezOngQwVaESGyGQR93EtnVxfSL68XA+NZdLwYgxgX55eDNh+TRrT+vkylQ1IN0RCm37ig9PZ2bbrqJ+++/n/vvv59+/fo1GFObudLWgIrLZd1BXFlZSUJCQr1j2dnZ+7Tft956C4C//e1vTJ8+vd4xj8dDXt4+NkuLgH79+rFhwwa2b9/OQQcd1OB4bVZPY2Xrtm/f3uC5us839vfSFrV/hzNnzmTy5MntmktERHqe2mwigP4p7aulfdOpw3nzu534gyHmLNnGjAlD6JMYxfrcIiIiIiLSc5kmrFplZRK5OreaQbm3nB93/UiVv5LMhMyWT+gMoQAQarRHkYMaRtvXk2hUARA0bawPjaTMbJgFFcLOuuBIDrb/RKzhI87wMMq2kbWhUZi0fPO6y+7AhsHOqt0MiEtt/Q30cTFQUgolm6HPSRDhG+8jpfPz1kQ6wa233kpiYiJPPfVUo2XRjj76aJKTk/niiy/YvXt3q+ft27cvABs3bmxwbMGCBfu015ISKyWysbJrr7/+OqZp7tO8kVDb5+fll19ucMzv9/P666/XG1fXJ598QmlpaYPnX3nlFQAmTJjQrr2dccYZwN5AW2vUBvoCgUC71hYRka4vp06gqG/yvmcUgZWRdPk4q0yrtybEIwsa/h4gIiIiIiISEbm5sHkzZHZuoKa6xsMPeT+w27ObjIQMDLpIQMNsvFWHEz9j7WvDQaIa086a4OhGg0S1AjhZFxxNjWnlzyTbyhlu2wq07vprsjOOAm8Zpf7q1u8/Pga8QH421JS3/rxOpkCR9EhpaWn89re/xefzMWvWrAbH3W43f/zjH6moqGDSpEls3bq1wZicnBxeeOGFes+ddNJJANx///0Eg8Hw8y+//HKjwZTWOPBAqyn2rFmz6vUoWrt2LX/605/2ac5I+dWvfkVsbCyvvPIKH3zwQfj5UCjEX/7yF3JycjjyyCMbLd9XWVnJLbfcUi8o89FHH/Haa68RGxvL1Vdf3a69TZ48mTFjxvDiiy9y77331utfBFZvisWLF7N48eLwc+np6TidTrZs2VLv709ERHqeXXUCRe0pPVfrxlOHk+C23ky8uiKbDXkV7Z5TRERERESkgfXroboa9vTs7gz+gJ8f834gvyqfzIRMbF0pbBBq7IZvk1H2DcQZ1vs+v+lkTfAgKmn5a+YlhnXBkQRN6zX2thUxyNa6SlGxDhe+YIBdnpJWbx+3E2oMKC60+hR1UV3ob1wksm699VaSkpLweDyNHr/99tuZNm0aX3zxBaNHj+bYY4/lsssuY/LkyYwdO5aBAwfy0EMP1TvnhhtuoHfv3rzxxhuMGTOGiy++mMMOO4xp06bxu9/9bp/2efXVV5OZmcl7773HyJEjueSSSzjjjDM47LDDOOGEExg8ePA+zRsJgwYN4sknnyQUCvGLX/yCE044gcsvv5wxY8bw0EMPkZGRwdy5cxs9d+rUqcybN48DDzyQyy67jJNPPpmJEycSDAb5f//v/zWaQdUWDoeDt99+m6FDh3LnnXcyaNAgzjjjDKZOncpZZ51FZmYmEyZMYMWKFeFzXC4XZ599Nnl5eRx66KFceeWVzJw5k2effbZdexERka4nt8wbftyvnaXnANIT3PzmlGEAhEz424fr2j2niIiIiIhIPRUVsHo1tLJneiQEQgFWF6xmZ/lOMuMzsTdS4i2qQl5+HsZwURPOJPKZLlYHD6KauFZPWUkim0LDqS3kNMCWS4aR36pz451usquK8QdbWbHIMAAblFWCV4EikU7Xq1cvfv/73zd53Gaz8fzzz/POO+9wxhlnsG3bNt58802+/vprYmJiuO2225g9e3a9czIyMvjyyy8577zz2LVrFx999BHJycksWLCA888/f5/2mZaWxooVK7j88svx+/28++675OTkcO+99+5zllIkTZs2ja+++orzzjuPdevW8cYbb+DxeLj++uv57rvvGDWq8Ybew4cPZ+nSpRxyyCHMnz+f5cuXc+yxx/Lee+8xc+bMiOxtxIgR/PDDD9x3330MGDCAb775hnnz5rFx40YOP/xwHn/8ca644op65zzzzDNMmzaN4uJiXnrpJWbNmsUXX3wRkf2IiEjXkVOvR1H7M4oAZowfGp7ry42FLNpQEJF5RUREREREAKvkXHEx9O7daUvmVuSytXQbveN647A5Om3dVgv6wFY/eOVmb2Wh3WYvfLT95sDdZirbQkPCnx9g20YiLVeOSHLEUl7jId9b1vrF4tyQ74XqHW3eZ2cxzGg2QBHKy8tJTk6mqKiItGYixV6vl23btjF06FBiYtQ8WbquOXPmcPXVV/PXv/6Vu+66K9rbkW5I/9+JSCRc9J/F/LCjFIAN952N2xGZu+Le+TGH373yIwAHZiTw4W9PwGHXvVciIiIiItJOwSC88ALs2gVDhnTKkjXBGhbvWEyZr4zecZ0XnGq9EJSuBtMEx96MoXSjiAPtmwHICg4i1+y3zysMtm2nv20XAB4zhpXBQwi1kF+T5yklMzaF43qPwDBa0cuppAJ8FfDLcTD2JnC0fDNjcXEx6enplJWVkZSU1KrX0h56VysiIiIiPU7unoyi3onuiAWJAM4/tB+HDkwBYGN+Ja99uzNic4uIiIiIyH4sKwu2b4fMzE5bMrcil8LqInrF9Oq0NdskFAAzBEbTGUU+3O1aYntoEBVmPACxhpeBrehXlOyMo9BbTqm/unWLxMeAFygs6LJ9irpgLpmIiIiIyL7zB0IUVFhvHPpFqOxcLcMwuGPiaKb8dykADy/YwPmH9SPBrV+rRURERESkHdauhVAIOqm6ij/oZ0vJFlx2Z9csOQcQqgEzALb6wSC34Q8//jw0lp1mBiYGJmBiZfhYnxvYCXKUsZVMo6lScQabg8M51L4Km2HSz9jFblKpILHJbcU6XBT7KtnlKaGXO77l1+FyQsCE0gorUBQ/qOVzOlkX/RcgIiKy/yn31jDrq20M6BXLlCMHtC59WUQayC/3hpuS9k+J/Juso4akcs7YTD76KY+iSj//XbSFP5w1MuLriIiIiIjIfqKoCNavh4yMTlsytzyX4upiMuI7b802M/dkFP2sR5GrTkbRM8EzKSK52Wk+5jAetT+Hwwg1etxDLDtCAxli34FhwHD7lhZL0CU43WRXFTMsMQO33dnyazGAUi9U50DqkS2P72QqPSciEXXVVVdhmqb6E4m0Ubm3hmmzlvPYwk3c9sYq5i7rug0ORbq6nD1l5wD6Jkc2o6jW7eeMwmm3grlPf7U1XOpORERERESkzTZuhLIySEnplOX8ASubyO1wd91sIgCzBjCB+jfSOowaAHymk+JmMn9q7SaRlebgZsfkmn3bVIIu0RFLeY2XAm95i+tbk8ZAoR+qsq1MqS6mC/8rEBER2T+Ue2u4ctZyVmaXhp+7+901jMpM5OghqdHbmEg3tatsb9Am0qXnag1Oi2f6cUN45utt+AIh/m/+Bh655LAOWUtERERERHowrxdWrrSCRJ1UWWRnxU6KPcX0TejbKevts1CAnweJwCR2T0ZRjpnGocZ2TjDW7yk0x54CdNafuaTyauh4AL42R3Ek25pZrG0l6Ow2G3bDRnZVMQPiUluuChMfA5WVUFoAvmKI7bxeVK2hjCIREZEoqvDWMH32cn7cEySy26xfLAIhk+vnfk9emTeKuxPpnnJL937fdETpuVo3nTqClDirxMBbP+Swamdph60lIiIiIiI91JYtkJfXaWXnfAEfW0q2EOuIxW7YWz4hmkL+Bk85COI0AgDkmOmMMnI5zraJY22bGWfbzDG2LRxj28LRtq1MNL4niWoAvjeHUm26ml3OQyzZoQEA4RJ0NhovVweQ4oyj0FtOib+q5dcSHwOeEOzebfUp6mIUKBIREYmS2iDRDztKAegV5+SdG8Zz/LA0AIoqfVw39zt8gWAUdynS/dQtPddRGUUAyXFOfnvqiPDn932wDrO2OZKIiIiIiEhLTBNWrwaHw/roBDkVOZR4SkiJSemU9dol6IOfBbPcdfoT5ZjppFLZ5OkOI8RxxkYAanCw3Bze4pI5Zr9Wl6CLcTjxBQPkeUpbnBeHHYIhKPeDJ6/l8Z1MgSIREZEoqPQFuOrZFXxfJ0j00jXHMrZ/Mv++/Aj677m4/WN2KXe+vUYXn0XaILeTAkUAVxw7mKHp1puI5dt2M39NfoeuJyIiIiIiPUhODmzdCpmdU4bMG/CyefcW4hxxXT+bCBNCjQSKjPqBol40n80zwbY+/Phrc2Qr1rVK0IVMq+JLP2MXiVQ0OTrB6WZHVTG+YCv6DtlsUBqEym1gNp2pFA0KFImIiHSySl+Aq2Yv57vtJQCkxDl5ceaxjO6bBEBqvIsnpx2J22H9mH7122xeXLYjavsV6W5qA0Uuh420+OZLC7SXy2Hj9nNGhT9/4KN1+ANd6xd+ERERERHpotats3oUJSR0ynI7y3dS6u0m2URm0OpR1FJGkdF0EAfgAAroi3X9ZZ05gGKz5a91W0rQJTpiqajxkO8ta3Fe4mOg0Av+EvCXtjy+EylQJCIi0omqfAGufnY53+4JEiXHOnlx5jjG9EuqN25s/2QemHxw+PO731vDt1m7O3WvIt2RaZrklFiBov4psS03FI2AM8dkcMzQVACyiquZ+832Dl9TRERERES6ufJyWLMG0tM7ZTlvwMuW3VuId8ZjM7pBWMAMAMEWMop6k9pCRpFhwPg9WUUmBotblVXU+hJ0dpsNh2Enu6q45WowcW6oCkFJ1+tT1A3+RYiIiPQMVpBoBSuy6geJDuqX3Oj4iw4fwIzxQwGoCZpc/+L35Jd7O22/It1RuTdAld/q69UvJaZT1jQMg/+dODr8+WMLN1Fa3bDpqoiIiIiISNimTVBUBGlpnbLcjrIdlPnKSI5p/BpElxOqgVAjgSL2vtcqJhG3EWhxqvHGhvDjr0OjaF11f4PNwWGtKkGX7IyjyFtBib/5oBVxbqj2QbkHvAWt2USnUaBIRESkE1T7A1w9ZwXL92QFJcU4eHHmOMb2b/4XtL+cO4pjD7AyFQorfFw39zt8gWCH71eku6rXnyi5Y/sT1XXIgBQmHd4fgDJPDc8tUVaRiIiIiIg0IRCAlSutknO2jr9EX13jYWvJNiubqLuEBEIBIAQ/y35y7Sk9FzQNaszWlRrPMMo5kFwAdpLGDlqXxeUhrlUl6GIcTnyhALuqS5uf0G6HUAjKQ1CV1ao9dJZu8q9CRESke/vbB+tYvq1ukOjYFoNEAA67jccvP4L+KdYF7x92lHLXu2s6dK8i3dmusr2Bor4pnRcoArj5jAPDjz9Zm9epa4uIiIiISDeSlQU7d0JmZqcsl122g/LulE0EYNY0+rTLsDKK8ulFsuFpdExjxtvqZhW1rvwcNCxB19fY1ei4REcM2dXF+IKN7zvM6YASP/iKIFDd6n10NAWKpFszDKPeh81mIyUlhRNOOIFnnnmm5bqQUTJkyJBO6ZkgIl3Hog1W7VmXw8YLvxrHwQNa/8tZWoKbJ6cdidth/dh+eXk2Ly3b0SH7FOnuckr3lmfs30ml52oNTI3j4D0B4DW55eSVqVSkiIiIiIg04qefrMwSt7vDl6r2V7O1ZCsJzoTuk00EezKK6rMRxG1YgZgcM51UKls93bHGJuxYFVqWmCPDJeVaZpWgq73MnGErABpec050xlJR4yHfW9b8dHExUOgFXwX4S1q9/47Wjf5liDRt+vTpTJ8+nalTpzJmzBgWL17MNddcw+WXXx7trUWVAlIiXUMoZFJQYV0wPiA9nkMHprR5jrH9k7l/0sHhz//67k/8sKPr/EIh0lXUKz3XyRlFAKeO6hN+vHB9fqevLyIiIiIiXVxBAWzcCBkZnbLcjrIdVPgrSIpJ6pT1IibkBZruT5RjptOrDYGiRMPLYUYWACUksMYc0OpzPcRRalo3BcYYPpKM8gZjbIaBw7CTXVXcfPJCfAxU10BpGfh3t3oPHU2BIukR5syZw5w5c3jhhRdYsmQJ8+fPx+Fw8Morr/D+++9He3sisp/bXe2nJmj9kpCRtO8ZDpOOGMDV44cAUBM0ueW1lXj86lckUle0A0Wnj977Zu+zdV2rOamIiIiIiHQBGzdCeTmkpHT4UlX+KraVbiPRldi9sokAgr4G/Zvchi/8OMdMJ9VofaAIYIKxt/zcYrP15ecACsy9NwVmGI2/10t2xlHorWC3v5l9xbrB44MyL/iK27SHjtTN/nWItM4ZZ5zBtGnTAHj77bejuxkR2e/VLT+V2Y5AEcBfzh0dzkjaVlTFPz5e3675RHqaeoGi5M4PFB3UL4k+iVb5iK83FymYKyIiIiIie3k8sHIlpKZ2ynI7y3da2UTubpZNRAhCfsBR71k3dQNFvUmlqk2zHm5sI3bPHMvN4fhMRwtn7LXb7EXNnvFpxm7sNCyNF+Nw4g8FKPRWND2RzQDThEoTqne2af8dSYEi6bEOP/xwALKzs8PPGYbBkCFD8Pv93HPPPYwaNQq3282FF14YHpOdnc2vf/1rBg8ejNvtpk+fPkyaNIkVK1Y0WCMrKwvDMDj55JMpLy/nd7/7HQMHDiQmJobRo0fzyCOPEAqFmt3nM888wyGHHEJsbCyZmZn8+te/prS0tNGxgUCAJ554guOOO46kpCRiY2M57LDDePTRRwkE9v7ntGjRIgzDYPv27eHXXfsxZMiQVn4FRSRS8sv3BooyktsXKHLabTx08aHhfkVzlmSxZEtRu+YU6Uly9/QoSo13EeuytzA68mw2g9NGW3ea+QIhFm/W96fsn5ZsLmL219v4bH0+O4qrCYa6Zu9QERERkU61Y4dVeq537/bP5a+AQNN9UWuCNewo20GcMw6DbtaaIuQHMwC2n5We+1lGUa82ZhS5jCDjjM0AeHHxnTm01eea2Cg00wGwGSbpRuPv9dw2B8W+ZgJFAC4nFPnAVwQBT/NjO0nrQ2Yi3UxFhfUN6f5ZU7hQKMSFF17Il19+yUknncQhhxxCWloaAKtXr+bUU0+lqKiIkSNHMmnSJHbs2MFbb73Fe++9x0svvcTFF1/cYC2fz8epp57Kli1bOPXUU/H7/SxcuJBbbrmFlStXMmfOnEb3+Mc//pHHHnuMk08+meHDh7N48WKeeuop1q1bxxdffFGvv5DH42HixIl8/vnnpKamcuyxxxITE8OyZcu4+eab+fzzz3nrrbew2WxkZmYyffp03njjDaqqqpg+fXp4nvT09PZ+aUWkjfLKI5dRBDC8TwJ/PHsU976/FoDbXl/F/JtPJMGtH+uyfwsEQ+Hvt34p7f9e21enjcrg5eXWjSoL1xdw+pjOqT0u0lWszytn6qxl1C3N7nLYOCA9nmF9EhjWO4HhfRIY1jueA9ITohLUFREREYmKggIrm8TRzvfvnjwo+RHssdDrUHClNFyqupAyXxl94vo0ONblBTwQqgFHQr2n6/Yo2mmmk2q0LaMIYIKxnkXmQQAsNkdxPJtafW5BqDf9bHkAZNgKyQ9mNhjjtjkp93vwhwK4bE38PcfFwG4f+CrAXwKOzq+G8XO6oiQ9kmma4d5EhxxySL1j2dnZuN1uNmzYQP/+/eudM3XqVIqKivjjH//IAw88EA7UvPnmm/zyl79kxowZTJgwgb59+9ab85tvvuGQQw5h06ZN4UDMli1bOPHEE3nuuee48MIL62Ut1XrhhRdYtWoVI0daNTGLioo47rjj+Oqrr/j888859dRTw2P/8Ic/8Pnnn3PJJZfw5JNPkpxsNVCrqKjg0ksv5d133+Wpp57iuuuuY9SoUcyZM4dFixZRVVXVZKBKRDpHft3Sc8nuZka23tXHD+GTNXks27abnFIPf/tgLfdPOqTlE0V6sIIKXzhrIRpl52qNH56O22HDFwjx2fp8THNsvZs/RHq65dt28/P+vf5AiPV5FazPq393pcth474Lx/LLowZ24g5FREREoiQrC+Li2jdH1XYoWWVl3AQqoWiZFSyK3Ru0ME2TnWU7MQwbjqaCFV1ZsDYAVP99VN2MogIzhUTano0zysghjQqKSWSlOZhyM5Yko3XzVBNPpRlPglFFglFFHFVUE19vTIzDRYmvksoaL6nuhMYninVBWbXVq8q/G+L6tfl1RJpKz0mPEgwG2bRpEzNmzGDp0qW43W6uvvrqBuPuv//+ekEisMq1rV69mkGDBnHffffVu6AzefJkLrzwQiorK5k9e3aja//zn/+sl60zbNgw7rjjDgD+/e9/N3rOvffeGw4SgZXtc9111wHw5Zdfhp8vKCjg6aefZuDAgTz77LPhIBFAYmIis2bNwuVy8cQTTzT5tRGR6KmbUZQRgYwisMpb/fPiQ4nbcxf2y8uz+XxD480URfYXu8rq9CdKiV6gKNZlZ/xw63eC/HIfa3LLo7YXkWjIKqoOP77wsH6cfVAmw/sk4LA1DJj6AyH+9sE6yr01nblFERERkc5XUQGFhZC0j/2CzBCUb4Td3wMGxGRATF8IeqB4OVRspfZunXJfOflVeSS7k5ufs0sywV8ONmeDI7U9iorNRGIIsC/349kMGG9sACCEjaXmiDadXxDaWzawj62wwXGXzU6NGaSymbKAxLjA64MKL/h2t2n9jtINw4nSlG2TpxAo6h518B3p6Qx9842IzdfYXbqJiYk899xzDBs2rMHYX/ziFw3Gf/XVVwD88pe/xOls+B/RtGnTmDdvXnhcXampqZxxxhkNnr/sssu4/vrrWbJkCaFQCJutfmz2zDPPbHDOgQceCMCuXbvCzy1atIiamhrOPvtsYmMbXvjKzMxkxIgRrF69Go/H0+gYEYmevPK9d7xEovRcrYGpcfzvxDH85a3VAPzpjVV8cvOJpMS5IraGSHeSU7r3F/Folp4DOHVUHz5bbwVvP12Xz9j+3fENmsi+ySreWwLkT+eMou+eDL+aYIgdu6vZUlDJ5sJKPlmTz4/ZpZR5apj99TZ+f/qB0dqyiIiISMcrLLQySPalP1EoCGXroGK9VY7NuSfYZBgQ0wf8pVDyAwSqIHk0uyry8Aa8pMd2wxYUIS+EPGD7eUUWE9ee0nM5ZjqptK0/UV0TbOt5N3gUAF+HRnGWbVWrzy000xlibsdmmPQ2itjOIMyf5eMYQIXfw8+Sjfay2yFkQjXg2blvLyLCFCjqQQJFRQTy86O9jaio7cFjs9lISkri4IMPZtKkSfTq1avB2D59+jToWwSQm5sLwJAhQxpdo/b5nJycBscGDx7c6DnJycmkpKRQWlpKSUlJuBdSrQEDBjQ4JzExEbD6HtXKysoC4Omnn+bpp59udK1au3fvbpAtJSLRVVt6zmW3kRof2SDOZccM5OM1eXy5sZCCCh93vbuGRy89PKJriHQXuaVdI6MI4LTRffjft63HC9cV6AK47FdqA0UxThsZiXuDtk67jWG9rR5FZwLnHdyPUx9aRCBkMuurbVx1/BDd7CAiIiI9V2EhBINt708U8kPJT1C1BZy9wNFI6TpXCgRcUL4Ov7+CHWUlxDubilJ0cYHqJvsT1eYK5JrppBr7HigaYOxmMIVspzdbyGSXmUJfo7RV5wZxUGym0tsoxmkESDVKKDbrX/N125wU+1rYnwFUmuAthKAP7JFpVbCvFCjqQRzp3SdCHOm9tqUHT0zMvt1h3BG9BX6eYdSUUCgEwGGHHcahhx7a7NjGgmAiEl21pef6JLkj/n+JYRg8OPkQznzkC8q9Ad7+MZezDsrknIP7tnyySA/TlQJFfZNjOahfEmtyy1mdU0Z+uTdipSdFurJAMET2bqv03ODUeGyNlJurNSgtjouPGsDLy7Op8AV45qtt/OGskU2OFxEREenWduyAtl6XDHigdBVUZYG7N9ibOd8RBzYHBSVrKSsvJTPt4HZtN2oCVVhRlJ/1J2LvTfVWRlEV7THBtp7te8rILQ6NZIp9WavPLTD70JtiAPoYhQ0CRTF2J5UBL96gnxh7EzdCxbihxG+9Xn9JvR5T0aBAUQ8SyVJu+6N+/aymYdu3b2/0eG1WT2PZOjt27Gj0nPLyckpLS4mNjSUlJWWf91abeTRhwgT+9a9/7fM8ItL5vDVByjxW34VIlp2rKzM5hrsvOIibX10JwP+8/RNHD00lPUGBY9m/1A0U9Y9yoAjgtFF9wv2JPltfwGXHDIryjkQ63q4yLzVBqzb+kPSWGzXfcMpw3vhuJzVBk2cXb2PGhKERz74VERGJtpIqP/d/tA63w84lRw9UWeL9kdcLubmwp5JQq9RUWuXkPLusfkS2ln9HMg0n2UE79qAHe9VWiB8MzpR933enC0FNE/2JjPqBol7tyCgCON7YyEuMx8TG1+YoJpvLWt3zqMxMwmu6iDH8pBiluPDhZ+81mBi7kyKfl8oaX9OBolgXlHrBVwX+3VEPFLUunUFkP3DCCScA8PrrrxMMBhscnzt3br1xdRUXF7Nw4cIGz7/yyisAHHfccdjt9n3e2ymnnILdbuf999+npqb1jX5dLus/okAgsM9ri0j75JXt7ZmSkdxx2QQXHtafsw7KAGB3lZ+/zFuNuaeJpcj+orZHkdNu0LsLBEpPG50RfrxwXUEUdyLSebYV7b2zc0hay+VOBvSK49KjrSBqlT/Ik19u6bC9iYiIRMsf31zFa9/u5IVvtnPev75myhNLeHdlLv5AKNpbk85SVGT1J0pKat14fwkUL98TJMpsVZAIoNRfTYG3gqS4PlbJusot4Ctsx8Y7WdBrfTToTxT5jKJeRhVjDas/UAHJbKItgRqDglAf65EBvY2iekcdNjsBM0Rljbexky0xbvD4ocILvt1t3X7EKVAkssfJJ5/MwQcfTFZWFnfeeWe9C6xvvfUW8+bNIyEhgRkzZjR6/h/+8AeKi4vDn2/bto177rkHgBtuuKFde+vfvz8zZswgKyuLyy67jPxGelFt3ryZN998s95ztVlSGzZsaNf6IrLvasvOQcdlFIFVgu5vFx0cvgv7k7X5vP1jw55qIj1ZbUZRZnJMs+WuOsvB/ZPpnWi9wfl6cyHemoY3ooj0NNuL6wSK0ltXF/+GU4bjclhvTZ9fsp3CCl8LZ4iIiHQfn6zJY8Ha+tdxvt1ewm9f/oHx//iMRxZsJL+8mYvJ0jMUFEBNDbSmZUTAA7u/3ZNl0g9srS8KtstTgi8YIM7hBmcyYED1DitLpzsIesAMNPqa3YY//HinmU6qUdHu5cYb68OPvw6NatO5hWZvai8fZ9gKgPo369oMg/IaT8MTa8W4wOeHasAT/es3ChSJ7GEYBi+++CJpaWn8/e9/56CDDuLyyy9nwoQJTJo0CZvNxqxZs+jbt2Hfj2OPPRabzcbw4cOZPHky559/PmPHjiUnJ4crrriCSZMmtXt/jz32GGeccQZvvvkmw4YNY8KECVx++eVccMEFjBgxghEjRvDCCy/UO+f8888H4LTTTuOyyy5j5syZ3H777e3ei4i0Xn4nBYoA0hPc/P2iseHP73xnDbvKmvmlRKQHqfIFwmUe+yVHv+wcgM1mcOpI6y4zb02IpVuKWzhDpPvLKq4OPx6c1nLpObCCu1PHWVlFnpog//1CWUUiItIzVPkC3PXumvDnVxw7iJEZe0uPFVb4eGzhJsY/8Bk3vvQ9K7J2qzJET5WbC62tNlS9E3wlViaR0frL9/5ggOyqYuKddYJRjgQIBaE628ow6upqKmmsPxE0zCjq1c6MIoCjjS24sN5HfmOOIGC2/uvtw02ZaZWRjDF8JFE/cOU2nBT5Kpr+nrYZVmyp2gBvPgSj+/ejQJFIHQcffDDff/8911xzDZWVlbzxxhts2LCBCy+8kMWLF/PLX/6y0fPcbjefffYZl19+Od988w3z589n4MCB/POf/2TOnDkR2VtsbCwfffQRzz33HOPGjWPdunW88cYbfPvtt/Tu3Zu7776bBx98sN45v/3tb/nf//1fEhISePPNN5k1a1a4HJ6IdI7OKj1X6+yxfbnwMCubsMIb4L4P1nX4miJdQd2gaFfoT1TrtNF9wo8/XdcwI1ikp8lqY+m5WtefPIwYp/X2dO4323VntYiI9AiPLNhI7p73hCeMSOfeC8by8e9P4JVrj+WcsZnY92TBB0Im76/axcX/Xcp5//qaDXntz5SQLiQQgO3bW1d2LuiFym1gj2tTkAgg31tGeY2XJMfP3g+5kiFQAZ6dQFcud1jbn6jxMnu1PYqqTDelJEQkUBRr1HCUsRWASmJZZbatr2yB2Tv8uI+tfrnxWIcTT9CHp7kAkM0GFUGoqbLKDUZR6/PWRLqgtt5l0ZrxgwYN4qmnnmrzXpKTk3n88cd5/PHHWxyblZXV5LGTTz65yX3a7XauvPJKrrzyylbtyeFwcO+993Lvvfe2aryIRF5nlZ6r6+7zx/LVpiKKq/wsWJNPubeGpJiGjSBFepLa/kQAfVM653utNSaMSMflsOEPhPhsfQGmaWK0tkOqSDeUtaf0nNtha9PPvT6JMVx53BCe+nIrvkCI/3y+mbsvGNvyiSIiIl3Umtwynl2SBVg/F++7cGz498BjD0jj2APSyC318NKyHby8fAfFVf4955Uz5b9LePrKozj2gLRobV8iqbjY6k/Up0/LY6tzoabUKjnXBqZpkl1VjN2wYbf9PMBkgCMRvIVgj4eYjEbniLqAB0I+sDd245+Ja09GUY6ZTjLVOIzIBL2OMzayxBwJwHJzOEeQ1epzi81UAqYdhxEkzdjNNgIE94Rc3DYnpTXVVNZ4rVKAjYlxQWG1VXLPXwKx0fu7UUaRiIhIB+rM0nO1kuOcnHeIVSbTHwyxUFkMsh+o7U8E0K8LZRTFuRwcP8x6g7+rzMvaXd2kNrjIPgiGTLJ3W9+Lg9Pi2twr7NcnHkCcyyrJ8vLybHJKVT5VRES6p2DI5H/e+olgyLoR+KZThzO4kUzbfimx/OGskSz586k8csmhHJiRAFjVIa6ctZwPVu3q1H1LBykshOpqiG3hfUrID1VbrUBJG7OJSvxVFHrLSXE2UfrX5rQydTw7reyirihYvac/UcMbXZ3UYDes76dIlZ2rdbCxgxisQO135gFtKj9nYqPQTAfAboRIN/aWG7fbbJimSWWgmUz5ODdUesDrt3pSRZECRSIiIh2obum5PkmtaFoZIecevLef2ger8jptXZFo6aqBIoDTRu29c3DhuoJmRop0b7mlHvxB687OtpSdq5WW4Oaq44cA1o0O//5scyS3JyIi0mleWr6DH7NLARjeJ4FrTxzW7Hi3w85Fhw/grd+M5+SRVikrfzDEjS9/z5zF2zp6u9LR8vKs/kQtVRbw5FpZJc6UNi+xq7oUXyhAjKOZaiKOeKtfUVUX7VcUqKSpcIWbvfvNNdNJNSojtqzLCHKYkQVAFTGsM/u36fyC0N73e31shfWO2Qwbpf7qn5+yV4wLPD7wANU5bVo30hQoEhER6UD55VZqdK84JzHOVjaujICjhqTSO9EKTH25qZAKb02nrS0SDXUzD7pSjyKAU0fvLR+wcL0CRdJz1ZadAxiS3vZAEcA1JxxAgtsq1/H6t9lk727mjbWIiEgXVFDh5cGP14c//9uFY3E5WncJNt7t4Okrj+LiIwcAYJpw13truf+jdYRCbWu/IF2EaUJWFsS38LtRKAAVW62sH1vbrh14g36yq4tJdLSiiokzCQLlXbBfURBqKlrsTwS1GUWRCxQBHGPsvUFpuTm8TedWEUeVaWVyJRqVxLL399cYm5PdvkpCZhNfa7cTfDXgsYE3D0LRu3ajQJFIOwwZMgTTNFm0aFG0tyIiXVAoZIZLz2V0Utm5WnabwTljMwHwB0LKYpAer25GUd/krtOjCKzA1ajMRABWZpdSUNFM6QGRbiyreO+b4n3JKALoFe9ixoShgNXY+/8t3BSRvYmIiHSW+95fR4U3AMCUIwcwro19hpx2Gw9OOYQbT9l7sfrJL7Zy6+sr8Qe60oV9aZWSEusjKan5cZ5dVukxZ682L1HgKaeixkOisxU3zBm2vf2KvIUtj+8stf2JmgoUsTdQtNNMJy2CGUUAhxnbcWJ9364whxEy21JC2SA/1Dv8Wd2sIrfdiSfgpzrQRAZXbZZZFRCosjLKokSBIhERkQ5SXOUnsOeur8woXLiuV35utWpbS8+WW2oFX5JiHCTGNFNuIUpOr5NVtGh9F3pDJhJB24vqZBSlNVEfvxV+NWEoSTFWVtG8H3LYVhS5GvQiIiId6cuNhby7Mhewqkr85dzR+zSPYRj84ayR3HvhWGpb/r31Qw6/em4Flb5ApLYrnaGwECoqICGh6TGhIFRuBcMONkebpg+ZIXZUF+Mw7NhaKm1XK9yvKKfr9CsKVoMZbPL1u429gZaI9igKmThzq0n9No9b1r2GPRSknDg20rflc+soMtPDwaU+RiHGnmytGLsTb6iGikAzvTcddij1Q9CjQJGIiEhPVJtNBJDZyRlFAEcPSSU9wSo/98VGlZ+TnisUMsP9wLpaf6Jap43eW7f603X5UdyJSMeJROk5gORYJ9eccABgNQNXVpGIiHQH3pogd7zzU/jzP587mtT4xrMjWmvasYN54oojce8pXffVpiIueXKpMtS7k4I91T1szVyG9+aDrxBcqW2evsRfRaG3nGRXG2/SccRb5e66Sr+imqb7E0H9jKIcM53UVpSe8xshPu5Vwb2DCng2o4RNMT5MrJt5bWV+YleWkPxBDglLinBvr+LkDd8z86f3AFgear6v2M8FcLLbtLLBnEaAXkaptY5hYJpQVeNr+uRYNxSUgRkC3+42rRtJChSJiIh0kNoL19D5peegYfm5z9QbRXqooiof/qB1x1ZX609U69ABKaQnWBcKvtpUhLcmGOUdiURebek5l8PW7hskrho/hJQ4KzvwnR9z2FzQRe52FRERacJ/Pt/M9j0/C48ZmhruM9ReZx2UyYszx5Eca/1cXJNbzqT/LCFLGbfdQ1YWxDUTxAmFrGwibFamTxvlVpcSCAWJse9DVQVnEgTKrMyiaPYrMgN7+hO5mxxS26OoxrRTQC96GU3/+w9g8nlyFf8zpIC30ivY6Q7wTZKH//QuYkHJLkJf5JC8II+YTRXYfPVf94Vbv+aM7ctZYQ7HbGNbsAJz782BfYy9VSScNju7/c0EtmJcUO0BL+CNXjUYBYpEREQ6SF7djKIo9UypV35ulcrPSc9UW3YOum5Gkc1mcMpI642DpybIN1uLo7wjkcgKhkx27Lk4Njg1DputLXXdG0qMcfLrE607OUMmPPqpsopERKTr2lxQyRNfbAHAaTf424VjMVpbBqwVjhqSypvXHxe+KWpniYffv/ojZluvZEvnqqiwSs8115/IVwDeAnC3PZvIG/Szs6qYBMc+Xm8wbOBIstb3Fe3bHJEQbL4/EezNKNplphLC1mhGUQiTpYnV3DmkgFf6lFHuCIFpcvC2EL99J8hT/wpyycIAaYV7b9oL2cDXPxbvyL1/RzetfJP03bvZQkaDNZpTaibjN62AXbJRBnuyl2LsTkr91QRDTQTjYt3g8YPXbvWqCkWnvKQCRSIiIh0k2qXnwLqTrbb83KKNhapnLT1Sbunees99U6LzvdYadcvPLVynDD/pWXaVecKZfe0pO1fXlccNJm1PyZ73V+1ibW55ROYVERGJJNM0+Z+3VlMTtC4KX3viAYzISIz4OsP7JDLvN8eH+wD+mF3KD9mlEV9HIqiwEMrLIbGJfw9mCCq3AWazQZKm5HvKqKjxkuhsx81ytf2KqrOhpmzf52mPQDUQApu90cN2AjgMK7iTY/YmBj9xdXoWmZh8H+/hnkGFzMkspdhpjTVMkzveMrjjlRAT1pq46lwO2ZoBs8+wMfMmOzddFuTlk22UDbe+t5yhIP+7/DnWVPdr4wsxKDetv2u7ESIe6yYqt92JJ+CnMtBEyUiXA2oCUG1YJfj8pW1cNzIUKBIREekgdUvPRSuj6Ofl5xaqN4r0QHUDRV219BzACSN647Jbv35/tr5Ad4BKj5JVVB1+XHsBq73i3Q6uP3lvffh/frIhIvOKiIhE0rzvc1i2zeorMig1jptOHdFha2UkxXDDKcPDnz+3JKvD1pIIKCqCYBAcjsaP+4rAmweuXm2eOhAKklVZhMvuwNbe7DVHvBW0qsqCQMu9fyIuUAE0HiQCcLM3KJRDOr32ZBOZmKyN8/L3gUU82a+EXe69kaAxlS7+9ZabgzfsfS7kslFyYDzvTE7g/itdfHyUjco4g1JniI9SK7npQj/FfayAXbq3nPHLvsMMtO09W4W5NyiYaFilk902B/5QoOlAUa2qEASrwB+dPkUKFImIiHSQvC6QUQT1y899uFrl56TnyakTKOqqpefAuuh97LA0wNrz+jz1XJGeI6t4b534SGUUAVxx7GD67bnZ4rP1BazIil6DXxERkca88M328ON7LxxLjLPpC96R8ItD+5G6J+P2g1W7KChv4eKzRM+OHRDTxLUA04TKLDCDYG/79YKc6t0UeMtIdUXo9y5nEgR9VrAo6GlxeMSYASuLxt5M2bk9/YkAcsx0Uo1KQpjMyizlsf672RFTEz4+zOPk1p1p/OkzJ302WDcymQZUHZlK2cT+cEgaJxqp3L8tgxtyUzm0MgbbnliQ1wm3XxJkd5xVlWXE7p2YP1bTlmZF9QNFVkCrtgxlZU0z36tOBxSVW2v5S1q9XiQpUCQiItJBakvPuRy2cEPuaKhXfm5DIVUqPyc9TG43CRQBnDaqbvk5ZfhJz7G9bqAoLXKBohinnd+ffmD48wc/Xq9sPBER6VKyd1sXo/slx3DSgb07fL0Yp51Ljx4IQCBk8uKyHR2+puwDrxdycpouO+fbDZ7cfcom8gVr2FSeh9vmxNFEuba2M8CVbGUUVWVZPYM6Q6AaQv5W9ScCyDHTSKWKRclVrEjc+z5wgM/BjTmp3LYznbFr/MSu2VtGr/rIVPxDE8C+N/PKjsEhVTH8ZlcqD2zL4OgK631kWYLBP34ZwLenEkRaVjGura3PsqoijqBprVObUQTgtDnY7Wtmnlg3FJUBDqtPURQoUCQiItJBakvPZSbFRLSRaVvZbQZnj7WaMPoCIRauV28U6VlyS63vNZsBGYnuKO+meafWCRR9vqEwijsRiaxtdUvPRTCjCGDSEf0Z1tuac0VWCYv0vSMiIl2EtyZIcZVVFqtvJ96wdMWxg7HbrPeYLy3fgT8Q6rS1pZWKiqz+RElJjR+vyrKyaext/3ezo7KY3b4qekUqmyjMAGeK1auoajuY/hbPaLfgnv5ERjOl536WUeR27WJe+t7elVfmp/A/O3pzcHUMrp0e4r7fm5FTfXAK/iEJzW4hOWhnRl4KZ5RYX89tfQ2ePHfv8bgfS3AUti5zz8RGJdZ6MYYP556yeTE2J2U1HmpCTdy4G+uCah/4nFYAMRRs1XqRpECRiIhIB/D4g5R7rV8Aoll2rla98nOrVH5OepZdZdadZJlJMTjsXfvX24GpcQxKtfq3rNtVTiikzAjpGWozilwOG30j/HPPYbdx65kjw58/OH+DvndERKRLKCjfewG7M/vS9kuJ5cwx1s2AhRU+PvpJ7/G6nMJC8PvB3ciNbP4S8ORYQZk2qg742FKRT7zDjd3WAe99DBs4kq0+OVU7rGBWR6opB6OJHk571O1RtNNMZWPfZdTseeknl8YzvjwOGwaOfC/xy4uovU3Xe2AivpFNBOp+xobBlKJkphRa478ea+O9Y6yZDBPivynCqG7d16Kx8nMxdieeoJ/KmiYytWLd4PGBx2ZlddWUNT6uA3Xtd9LSdoFq6z+brvwRqG75dbSSYRjhj6VLlzY57rXXXguPGzJkSKNj5s2bx7nnnkufPn1wOp2kp6dz0EEHMW3aNJ555hn8fus/pSFDhtRbtzUfP99vey1atAjDMLjqqqvaPVdnaM9+d+7cyXXXXcegQYNwu93069ePq666im3btjV73uLFizn33HNJTU0lISGBY445hueff34fX4FI29XtT5TRiW8YmjJuaBrpCVYq9+cbClR+TnoMb02QokrrZ3RXLztX68AM641DtT9Yr7+SSHcVCpls31N2Z1BqHDZb5LNozxmbycH9kwEryPq+eu6JiEgXUHvDEhDxGyVaMv34IeHHc5Zkdera0go5OeBoIgBStQOCXnDEtXnarMpCyms8JLvafm6r2ezgSAJfEVTvBDooYy3kh0BVs2XnoH5GUUHqWkpirSBKH7+dyUXWeyv7bh8JSwsx9mzVNyQez8Epbd7SGaUJzMhLwTANXjzFxsoh1u+1Nl+IhCWFEGz5a1Fh7s1gqi0/57I7CISCVAaayExy2K25q0zra+Lv/L6czYfrpHsJVEPWi1H5h9QmrlQYMnWf/jNszosvvshxxx3X6LG5c+c2e+6vfvUrZs+eDcDhhx/OSSedhGmarFmzhrlz5zJ37lzOO+88MjMzmTJlCkVFRfXO//HHH1m5ciXDhg1jwoQJkXlBwk8//cQpp5xCUVERQ4YM4bzzzmPLli0899xzvPXWW3z55ZcceuihDc578803ueSSSwiFQpx44omkp6ezcOFCpk+fzqpVq/jnP/8ZhVcj+5vasnMAmUnRL4VltxmcdVAmLy7bgS8Q4rP1Bfzi0H7R3pZIu+2q873WXQJFIzMT+HRPf6KN+RUMTO3AN3kinWBXuTdc8iaS/YnqMgyDP549kmmzlgPw0CcbOGdsJs4unkUoIiI9W90bBDszowhg3NBURmUmsj6vgh92lLJqZymHDEjp1D1IEwIB2L698bJz/nIrULQP2URl/mq2VRaQ5IzF1tHl7W0OcCSCN88qCxfXn4jnnAQ9VrDImdzssNoeRUucqdh7LwKsLJ+r83vhMm3YKmpI+LoQI2BlnPv7xlJ9RCrs49doXEUcwUACc/oV8OiFPu6fEySzFBylNcR9t5vqo9OanbuykYyiWhU1LdwoWOEB07R6WBlt71/VHgoU9SQhnxUkssWCo4teKAl4rD2GfEBkLorY7XbGjBnDq6++yqOPPorjZ9H64uJiPv74Y4444gi+//77BufPmzeP2bNnk5yczIcffsjxxx9f73hWVhazZs3CvSdVtLEgw1133cXKlSuZMGECc+bMicjr2t+Zpsnll19OUVERM2bM4Mknnwz/3f7rX//it7/9LZdffjmrVq3Cbt9bx3T37t3MmDGDYDDIm2++yaRJkwDIz89nwoQJPPTQQ5x33nmcfPLJ0XhZsh/Jr5tR1AVKzwFMPLhvuNHph6t3KVAkPUJunYyc7hIoqs0oAtiQX8FpozOiuBuR9tteVBV+PCSt4wKfE4anc9wBaSzdWsz24mpe+zabqeMGd9h6IiIiLantlQnQN7lzfxc1DIPpxw/hz/NWA1ZW0cO/PKxT9yBNKC62+hP16dPwWHW2FSBxpbZ52q2VBVQF/PSP7aQAgs0J9nirZ47NATGZQAQDVMFqwLTK3TXBIITLqMEP3Ns7EcOwevecXZLAAV4XhidAwlcF2PzWTUs16W6qjk2zGti2w/GeEG9vn0zJwHf5vymV/O25IDE14N5RTSAjBv/gpvse1eDEY8YQa3hJoBKDECY23DYnxb7KJs8jxgUFJTCmD3jzIXZYu15DW+n2q57IEQuOhC760TE/NKdOnUpRURHz589vcOzVV1+lpqaGK664otFz33zzTQBuvPHGBkEisErN3XvvvfTq1blR3P3d4sWLWb16NampqTz22GP1AoA33XQTxx9/PGvXruX999+vd94zzzxDeXk5F1xwQThIBJCRkcGDDz4IwEMPPdQ5L0L2a9G8s6wpxwxNJS1+b/m5ar/Kz0n3l1MvUNQ1vtdaUjdQtDGvIoo7EYmMbcV1AkXpHZNRBNYFsdvO3tur6P8t3IS3pvMb/YqIiNTKq1N6Lhrv+y48rD/JsU4A3l+5i6LKJvqfSOcqLASPB2J/dh00UAXV28GZ1OZsl2JfBTsqi0h1xUekrUWr2d1gi4GqbKsUXSTVlLW6P9HjvZLZ6bYyhgZ4nZxXnIjhD5L4VSH2auv3wUCyk6rje0OEMs7H+6uozrqe7clpPH5enTm3NxPs2aO2/JzNMInH+l3ZbXdSUePBF6xp/KQYF+yuAGKsHlZmB5X8a4ICRdIjXH755RiG0WiJublz55KQkMAFF1zQ6LmFhYUA9O7du0P32Jk+/vhjJk6cSO/evXG73RxwwAHccsstFBcX1xt3/vnnYxgGH330UaPzBINBMjIycLlcDc5dt24dV111FQMHDsTtdpORkcGll17KmjVrIvIavvvuOwCOPPJIEhIaRulPOeUUAN555516z3/wwQcATJkypcE5EydOJCYmhk8//RSvt4maoCIRUr/0XNe4eO2w2zhrbCYA3hqr/JxId1cvo6iT7+LcVwf0jse+5w63jfktv8kQ6eq2F+/tQdpRpedqHTGoF2fsad6dX+7jOfVkEBGRKKpbBrlvFAJFsS47lx49EAB/MMTLeypISJTl5VmBoJ8HdKpzwF9h3UzfBiEzxObyfPxmkDhHFErbO2Kt7KKq7eDdBWYEbjoN+azKU7bmX4/b8PGj28Wc5D1l/EwbV+en4KgxSfi6EHu5FXQJxjuoPKEPpqupcIfZ5i0eY2zGrEmnOut6vhvch9w9OQSxhX4MX/M3K1U0Un4u1u7EG6xpuk9RrBs8PvDaoabC+uhEChRJjzBw4EBOPPFE3n33XSor915w2bp1K0uXLuWiiy4iLq7xMhgDB1o/UJ9//vl653ZXt99+O+eccw6ffvopI0eO5Pzzz8fhcPDII48wbtw48vPzw2OnTp0KwEsvvdToXAsWLKCgoICzzz6btLS08PNvv/02hx9+OM899xzp6emcf/75DB06lNdee41jjjmGL7/8st2vo6rKirY3lclVu5+VK1fWe7728yOOOKLBOS6Xi7Fjx+L1etm4cWO79yjSnK5Yeg6s8nO1PlilRuDS/XXH0nNuhz1cnmtzYSXBUNvftIh0Jdvqlp5L7/ieW7edNTJ83eU/i7ZQ5mnirkwREZEOVltJwmZA78To9Ka94tjB4Spbc5dtpybYuVkI8jOmCVlZ8PObnoNeqNwGzoRmS601psBbTk71btJcbQswRZQj3sr+qdoO5RvAXwy0499aYE9/Irur2WGm4eUvvdMI7fnlL6HwWAZW2kn8sgDHbivbKOS2UXlCb8wYe8MJQkHwl1gfbczQ6WuUMpAizGAiJTtuZPUwa36bCdWFVc2eW5tRBJBoWAEfh81OwAxRWdNEoCjGBR4/VGNln/lL27Tf9lKgSHqMK664gurqaubNmxd+7sUXXwwfa8qMGTOw2Wx8//33HHDAAVx//fW88MILrF27FtPsXhduXn/9df7xj38wduxY1qxZw9dff83rr7/Ohg0buPPOO9myZQu/+93vwuPPP/98EhMTefvtt6murm4wX+3XrzagBFbPpiuuuAKn08mCBQv44YcfeP311/nmm2/48MMPw2X+/H5/u15LbYbX9u3bGz2+bdu2BsfLy8spKysDYMCAAY2eV/t8U/OKREpeFw0UjRuaSqrKz0kPUrcufP9uEigCGJlp3WHmD4TYXtz8mwyRrq7237DLbuuU/gwHZiRy0eH9ASjz1PD0l1s7fE0REZHG1GYU9U5044xQuau2GpgaF+55mV/uY/6avKjsQ/YoLYWSEkhKqv+8J9cqteZMbtN0wZCVTWQCMXZnxLa5Txxx4EyxeixVbIHKrRDYx6yX2v5ELfQ8mpteQ7bTet0JnjRG5A0h4asCHCV7gkQuG5Un9CGU0MjXJuiDQBm4eoEjEWrK27zNo40t1oNQDLkZA8PPe1qoDFFNHAHTCixZgSLrGrPNMCj3exo/yW6zAo2VPiuoVVPa5v22hwJF0mNMmTIFt9sdDm6AFejo27cvp512WpPnjR8/nrlz55KWlkZhYSH//e9/ufLKKznooIPo27cvf/7zn8PBh67ub3/7GwAvv/wyw4cPDz9vGAZ33XUXhx12GG+88QZFRVZN0djYWCZNmkRlZSXvvvtuvbmqq6t5++23SUxM5Pzzzw8//+ijj1JVVcX999/P6aefXu+cs88+m+uvv57s7OxwCbh9deKJJwKwYsUK1q5d22Bvr732GgAVFXt/INXNCGsqgyw+Pr7BeSIdIX/PG4a0eBcuR9f5ceuw2zjrIJWfk54jd09d+HiXnaTY5utbdyX1+hTl62eSdF+hkBkuPTcoLS5cVrGj3Xz6gTjt1lqzF2+jsEI9GUREpHP5A6FwT6DOuFGiOVcdPyT8WGVZo6ygACoq6mcUhWqsbCJ7bJuziXI9JeR5SklzJ7Y8uDMYNqvHkiPe6llUsRGqd1il5FrNtLJljOYDX2vjvHyQbH29YkMhem87iV99/d7eIJHbRsVJfQim/DwrybSCQiEvxA2AhAMgfhDY7FamThscY9scfrzafRbley439t8ZoCrUXPk5g8o9WUVuowbXnl5LbsNBsb+i6eQEmwGlldZeI90TqgVd58qVSDulpKQwceJEFi5cSF5eHitWrGDDhg1ceuml2O2NpB7Wcdlll7F9+3bmzp3L1VdfzdixYzEMg/z8fB544AGOOuooCgq69sXUgoICVq5cyYgRIxg7dmyD44ZhMH78eILBYLj/DzRdfu6dd96hsrKSSZMmEVun+d4nn3wCwKRJkxrdxwknnADA8uXL2/V6Ro4cyUUXXUQoFOL888/ns88+o6KigpUrVzJx4sRwzySbTf+NSdcTCpkU7Llg1ZWyiWrVLT/34WqVn5PuyzTNcOm5fimxndvUtZ3qBoo25HX/0rey/8or9+ILWGU8aksqdoaBqXFcfswgAKr9QR7/fHMLZ4iIiERWQYWX2mu90ehPVNfxw9IY0ce6KL0iq4Q1ud3jhuceqfb6Yd3rVZ5d4Nvd5myimlCALRV5OG12XLbmr212OpsTXKlWsMeTC+XrwZcPZnPBkxCYNRCohJAHbE2Xnau2hXiuT2n48z/klHH951/Qp6zEmslto+KkDELJP5ujttSczQkJwyC2v1Uyz5EIsQOscneh1pctHkgxGVj7WB8aRvYga72YGthaWdr0iUAFDcvPxThcVAX8eJvag9sFBSVWHytP52YHdp/bLkVa4YorrmDevHm88sor4dJkzZWdqys+Pp6pU6eGAyf5+fk8++yz3HPPPWzevJn/+Z//4emnn+6wvdf1wAMPsH79+nrPjRo1ittvv73Jc7KysuD/s3fecVKU9x9/z2y9XrkCdxzl6L13BRSQYolgVNBgQY01lmg0icbEJJZYfsYYjYodUUEsYAMRlF6kSO93HFzvd9t3Z35/zG3jCtd395z363Wv252d8mybnef5PJ/PFzh27Nh5B8rcjiKAqVOnkpqayjfffENpaSnx8fFA3bFzvsfp0qVLo49RFxs3buSNN96otfzZZ58lMTERgMWLF1NSUsKPP/7o5wqLiorimWee4f777/erYRTpM1vDbDYTfa7NF2/to6ioIJmJodIhKTbZcNbUHEkJcIehLsb2UOLnSk12vj+sxM+F69VLApXQo8zswOpQBqhDpT6RGz9HUaHqKFIJXbJ8ohMzEiLa9dh3Te3FxzvPYHG4WLItm5sndic9vv3EKhUVFRWVXzb5Fd4I5ED3+wRB4Dfju/HoZ/sBxVX0zLwhAW3TL5asLPBNuZFciptI1IHYtH53jqmEQmsVKcbYVm1iq6IxgsagOHWqT4GuFPSximAkORVhSHKC7FTi1GQJkJRYOEP9NZc+6lRBuU7p600ttjDsi3CclUrddcmooeqCJKTocxxJLqsSaaePh/B0xcHli7GT8ri1QInQa4S7SxBgtHCclfJIZETyE3sxgAPKY3kWXNEymnri86pkb58vSqimRE7EKOqodFiodlgJq6s+U5geKkzgMihRhe2IOiqk0qGYNWsWsbGxvPvuu+Tm5tKvXz+GDx/erH0lJyfz8MMPEx4ezu9+97sWR6k1hW+++YYffvjBb9mFF17YoFAkScrJMyUlhRkzZjS4/4yMDM9tjUbDNddcwwsvvMCyZcu47bbbKC4u5ttvvyU1NZWpU6fWeZyFCxc2eIwxY8Y0+Pjx48d55513ai1//PHHPUJRXFwc69ev5+uvv2b9+vVUVFTQs2dPFixYwKFDhwAYMGCAZ9vo6GhiYmKoqKjgzJkz9O/fv9b+z5w5U+s1UFFpbQoqvJbrYHQUKfFzySzdnoPVIbHucBGzB6eef0MVlSDD7SaC0BOKuiWEo9eI2F0SR/NVoSiUKKm2oRVFYsIDnBEfJGQVe+tcdktsX6GoU5SBmyd25z/rjuNwybzw3VGe//XQdm2DioqKisovlzwfoSjQjiKAK4d14ZlvDlNldfLZnlwentnPU59WpZ2oroaiIv/6RLYCsBWBoVOTdmV12TleVUCYqEcb9Gk6guKAkV3gqPYROMQaMUao+S8q7h5BAE049dUn2h5pZmu00tfrUuHijo8EnJWKo8oWpsd6QQJSlO+1uAyOmjpA4WlgTFGOUwtRcRg5LUob9XF1rFOb0eJxVrpGAvB9/AVM0h5A74RBJyS2D7Mwylz3RKUq2SuERdc4ijSiiCRLVDksdDLWnuBOmAFKKsEsgKt9a9mGpFD0yiuv8Morr3icDQMGDOCxxx5j5syZAEyePLnWIPttt93Gq6++6rl/+vRpbr/9dtatW0dkZCQLFy7kySefRKv1viTr16/n/vvv58CBA6Snp/PnP/+ZG264wW+/L7/8Mv/617/Iz89nyJAhvPTSS4wePbptnrjKeTEYDFx11VUe588999zT4n26hZLzOWRak/Xr1zd5m7S0NAASExN5++23m7TtggULeOGFF1iyZAm33XYbH3/8MU6ns87YvrS0NE6cOMFzzz1HQkJCk9vp5oYbbqj1faoLQRCYNWsWs2bN8lu+ePFiQPm++zJkyBB+/PFHdu3aVUsocjgc7N+/H6PRSO/evZvddhWV85Ff6TOzLAiFIoBZg1JZuj0HUOLnVKFIJRQ56yMUdYkNzu9afWg1Ij06RXA4v4pTxSbsTimo6pmp1M17W7J4fOVBXJJM1/hwBnWJYVBaDIO6xDCwc8wvUjzK9nEUtWf0nJtbLujBe1uzqbA4+HT3We6ckknPTvXPTlVRUVFRUWkt/B1FgZ+0FGHQctWIdN7cdAq7U+LDHae5Y3Lm+TdUaT2KiqCyEjrViEKyBNVZgKg4ippAdnUxZTYTXcLjW72ZbYagAX3T4vXOJVfv4L1kRWiKq5J5eokTqVIZry8Mi6XqgiTio7zfPSQnOCsV4Sk8/fzij6iHiHSoOq64oLTnn+jUg0ISqaSYaPaK3ahINdIpx0qsGY7ZKhlJGEIdopcLLWY5jHDBQjhmRFxIaBAFkQqHpY4joUTPWe1gsoGunjpGbURI9kbT0tJ46qmn+Omnn9i5cydTp07l8ssv58CBA551brnlFvLy8jx/zzzzjOcxl8vF7NmzsdvtbN68mXfeeYe3336bxx57zLPOqVOnmD17NlOmTGHPnj3ce++9LFq0iG+//dazzkcffcT999/PX/7yF3bt2sWQIUOYMWNG0Ney6ehcf/31JCQkkJiYWCs2rS7qLR5Ww/HjSt75+aLWAk1aWhp9+/bl4MGDHD16tEnbjhgxgr59+7Jx40ZOnz5db+wcwLRp0wD49NNPW97oZmI2m1m8eDF6vb6Ws2n27NkALF++vNZ2q1atwmq1cvHFF2M0htaAokpo4ScUxRgC2JL6GdcjgbiaAc3vDxdidTSUI6yiEpz4OooCXUC4OfRJUaIInJLMqeL2nS2m0nTe25rNo58fwFUTLXq61MyX+/J46uvDLHhjG0P+tpoL/7WOOz/Yxas/nOBgbmWAW9w++H52u7Vz9BxATJiO2y7sAYAsw7KdZ9q9DSoqKioqv0yCzVEE8JtxGbirEby/JRunSwpsg35pFBWBywVuI4KtWKkz00jnipsSWxXHqwqI1oUhhlAd1pZiFSReTS3DLsrEV8o8/Z6EvqLGSRSu46GJtxMZeU5tH2elEjUX1avxr7M2SnEeSXbl7zwIAowSTgDgQsPppJ6ex9KyHZw01l/zyB0/JwoykSjXzUZRR6mtGkmu4/spCspFbZWF+hxXbUVICkWXXnops2bNolevXvTu3Zt//OMfREZGsnXrVs864eHhpKSkeP58a5WsXr2agwcP8v777zN06FBmzpzJE088wcsvv4zdrnw4Xn31Vbp3785zzz1Hv379uOuuu5g3bx4vvPCCZz/PP/88t9xyCzfeeCP9+/fn1VdfJTw8nDfffLP9XgyVWkyaNIni4mKKiooaFS+2aNEi/v73v5Obm1vrsSNHjvDAAw8AMG/evFZva2vz6KOPIkkSc+fOZc+ePbUeLykpqbfO0oIFC5BlmSeffJLNmzfTt29fRowYUWu9Bx54gLCwMH7/+9+zYsWKWo/bbDaWL1/uiXhrCUePHqWy0n+QpbS0lKuvvprTp0/zxz/+0eOkcrNo0SKio6P5/PPP/dpXWFjIQw895HkOKiptSYFPhyEYo+dAcTNM7pMEgMXh4nhhdYBbpNJUKiwO9p2pYPOJYr49kM8nP53h7U2n+M/3x3jyq0P88dN93L10N/d/tIfNx9vPFduehHL0HJxTp6hAjZ8LZpZsy/Zk/gP0SY7CqKvdlcouMfPlz4p4NOelDXzyU8cXLbJLlOg5vUYM2Pfw6pHpaEWlI/35nrMeMU9FRUVFRaUtya/0XosGS5JEt8QIJvdW3Cy5FVa+O1QQ4Bb9wigu9opEsgymbEBSavg0kkqHhd0lWVhdDmL0v5zaizIy7yaXU6B3MuiUxD/ek4itUK7pdBFOvr9gBKaIcPSCy28rQIn1O7ce0fkwJIAxWYmsk88/cXaUeNxz+6vkMcg1Gs6oozLfxdY/nuIbPxdVEz9n0OgwO2yYnfWIVFoNlFaCpn1d8iEZPeeLy+Vi2bJlmEwmxo0b51m+ZMkS3n//fVJSUrj00kt59NFHCa8pJLZlyxYGDRpEcnKyZ/0ZM2Zw++23c+DAAYYNG8aWLVu4+OKL/Y41Y8YM7r33XgDsdjs//fQTjzzyiOdxURS5+OKL2bJlS73ttdls2Gze2hXuQXCHw4HDUb/66HA4kGUZSZI8NWJqIUkgycqJqC5FMhiQZaWNkqT8tRL1vib1rON7u7i4mDfffJO//OUvDBgwgF69eqHRaDh9+jQ7duxAkiRGjx7Nn/70p3qP43Ylud+j8zF27Nh6H7vppptYtGhRo57Luce75ppr2L9/P08++SQjRoxg6NCh9OjRA1mWOXnyJD///DORkZHcfPPNtfZ5zTXX8Oijj3oiGufPn1/nc+nRowdLlizhuuuuY+7cuWRmZtK3b18iIiLIzc1l165dmEwmfvrpJzp37txge8/HkiVLeOaZZxg1ahSdO3emoqKCjRs3Ul1dzcKFC+t8T2JjY3njjTe45pprmDdvHpMnTyY+Pp61a9dSXl7OfffdxwUXXNCkdqi0L5IkIcsyDoejVvRhqJBb7q3XkBiubfD8Hkj6JHtnfh84W0afpF/ORWioIssy27PKWLrjDKsPFuBwNW4wdMXus0zunchDM3rTK6njRDKdKfV+15Iig/e7Vh89E7ydmUO5FVzSv2m55Srtw0c7z/Dnzw967v/2gu7cf3EmLknmRJGJ/bmVHMitZF9uJYfyqrA5lWsMSYbHvtjPyK7RISlkNgZJksmqiZ5LiwtDcjmRAmBQjTaIXNArke+PFJFXYWXj0QLG92x+RLKKioqKikpj8J20FB+mCZpr0evHpLPuSBEAb206xUV9EgPcol8QRUUQUdPPtpeDrVARMYTGjW1YXQ52l5+lzOUgOTwRqR43kSzDHjmDLDmRKeJBYoV6YsxCiO9jKjnjtPDgcolRx7z9XF2kk4wpxRzXpREvm5B8I/xkF2jDlD+xqeNHGojMAJyKWKSLoSEHT6ZcTIKrihKi2KrviyUhnPBiM51LocBmoUgvkOCsLbVU4HU5RYomJFmHXqelXHJQIbkw1PXZiAiHkmocQucmPqeWEbJC0b59+xg3bhxWq5XIyEg+/fRTTz2S+fPnk5GRQefOnfn555/5wx/+wJEjRzzugvz8fD+RCPDcz8/Pb3CdyspKLBYLZWVluFyuOtc5fPhwve1+8skn+etf/1pr+bp16zxCVl1otVpSUlKorq72uJ7ORXBUobNZwVGKrAnOE4TgsoBkxVFVhaxrvQHgc10ndVFVpai2kiT5rf/kk09y0UUX8f3333PkyBHWrl2LyWQiNjaWSZMmcfnll3Pdddc1eBy3+OdwOBrVlm3bttX72OTJk8+7D7PZXO/xHnroISZMmMAbb7zBtm3b2LdvH1FRUaSmpnLTTTdx+eWX17n/xMRERo0axY4dOwDFuVdfO6ZMmcLGjRt5+eWXWb9+Pd999x06nY6UlBRmzJjBpZdeSlpammf7htrbEKNHj2bq1Kns2bOHbdu2ERERwejRo7nxxhuZM2cO1dV1K/bTpk3jyy+/5Nlnn2Xnzp04HA769OnDLbfcwrXXXtukNqi0P3a7HYvFwo8//ojT6Qx0c5rFgZM1RRqBn7dt4ESQlqyoKBcA5Vz89ZZ9GPP2BrZBKvVS7YDtRQJbCkQKrc2zn68/WswPR4sYlywzM00iugPUtT2UrQEEBGT2bF7P/hDzyhdbwX05vuHn4/SxNy06VqXt2VoosPSE95r1os4Sfe3H+PrrY55lYcBIEUamgasLFJjh27Mie0pETDYXt77xA7f3k+iIqSHlNrDVdIjDXFV89dVXAWtLhuz9TfvPlzsoz1QnBamoqKiotC1ZBcq1aJRO5rvV3wS6OR4kGZKMGgqtAttOlfH+p18RH5yJ6B2P2FjlDyA8HOKuavo+uoMeKKvjIVmG/WUC35wROWNSLi436kdx/yAXxtCcZwvAGdNxdGve5PntEjqfSUdCspGMMVnowiXO2hKJiImnpN8trXz0aY1ec2i2yNpckBD5uf9Uxv64CoCRx2S+ntyNmWEza28kSwzadyd6l4lInYOSgYtAENABO+s7UFfln/mUub412oSQFYr69OnDnj17qKioYPny5SxcuJAffviB/v37c+utt3rWGzRoEKmpqVx00UWcOHGCnj17NrDXtueRRx7h/vvv99yvrKwkPT2dKVOmkJBQ/6w3q9VKTk4OkZGR9ddWcWohujPYSoHgFIoQAUNnjDGJoG35zHWXq/FTFqOjo+tcPzo6mr59+3LXXXc1ux3//Oc/+ec//3ne9ZrS3oaYNWtWg/uaOXMmM2fWcXI6D77xjedjyJAhvPbaa41a93ztrY/mPg+A6dOnM3369GZtqxJYrFYrYWFhXHDBBSFbS+ql45sAEwatyLzLZiIE6ejg6Gobrxz6AQB7eCKzZo0McItUfHG7hz7ccYZv63APxYXruLhfEvHheiINGqKMWiINWiJr/kcZlb+dWeU8/90x8ittyAhsLhDYU6bjlonduGlCBuH6kL0c5J/7fwBsdIoyctmcCwPdnCYjSTL/2r8Wq0OiSoxk1qyJgW6Sig8rdp/lw63eGqg3T8jgDzN6N+qc/muLg1n/2UxBpY0jFSLVyQO5emTaebcLNbadKoVdShd3VN9uzJrVN2BtucjhYvkzP1BldXKgQsfkiy8M6fObioqKikpw43RJ3Lf1OwAyOsUwa1b9yTGB4GTYcV5adxKA8G5DmDUsuOtudwjKy+GddxShSCdB8UYQDKA7fw1HSZI4UHGG45X5dAqLQXeOy0SWYbecwQrXKLJk/xSCAovA8h3Z3KNdHXoTk2QZV04lnfYXE+czF1wyajANTqBnt2x0ojL5J1dOZEDlYRJ++sG7osuqmIBi+uGeMNQsbCVgylLi68T6Z1ReLMWzlqsB+MaYgvtbP+qoxN9Hb2beodMY5dqzF6sFA/GCCYOzii67XsJKGIXWSrqExzM8oVvtA9kdUFhGyeRBzX9OzSBkr5z1ej2ZmZkAjBgxgh07dvDiiy/yv//9r9a6Y8aMAeD48eP07NmTlJQUtm/f7rdOQYGS2ZmSkuL5717mu050dDRhYWFoNBo0Gk2d67j3URcGgwGDobaMr9Pp0Onqn3LucrkQBAFRFBHFeqbL6iOh+3Ug2ep+PFgQDa0iEqmoqHRMRFFEEITznheDmYIq5TycEmNErw9e20ZqnI7ESD3F1XaOFlSH7Ovd0ZAkmXe3ZPHu1mxOFplqPT6uRwLXjunKjAHJGLTnvxjukRTDZcPSWLzxFK+sP0G1zYnZ7uLF70/w4c4zPDC9D3OHp6ERQ6tXYXdKFFYr37XOsWEh+/ntlRTFvrMVZJeacSFibEXHtUrzWbHrDA9/eoCadGFumtCdP8/p12jhP0Gn46m5g7nxLcWp/eTXR5jcN5m0uI51DZxT7u139EyKCuj3UKfTMWdwZ5ZuP43Z7uL7oyX8aljHE+dUVFRUVIKDYrMFd0m81CC8Fp3YK8kjFG3LKufq0d0C26BfAhYLmEyQnAymI0qcWVj0eevfyLLM4YpcTpTnkKiPwiBLnpIiikDUjU+kMZzCm2olIHGb5ksyhTO85prDDrknX7sGMkfc3aZPsTXRlNoI21OGrtSbnOUUwd4rClvfGNCJhAlK/WebrKOYaBI4jCj5RDy6zCCGgQTQgsn5uhjQxYM1F3SxINQ99p5BAekUk0Mi28L7Yo02YKy0kZkLBovE1sgKplbUjnqvEiKI15QCECOXYZe1GJCotJajkRyI5x5PK4DFis7cvkaQEAvoqB9Jkvxq//iyZ88eAFJTUwEYN24c+/bto7Cw0LPOmjVriI6O9sTXjRs3jrVr1/rtZ82aNZ46SHq9nhEjRvitI0kSa9eu9auV1O5ow0EfF9x/qkikoqLSgTHbnVRZlci85CApaNoQfVKiACiutlNUFeQTDX4hvLj2GI+vPOgnEsWF67hlUne+f+BClt46lsuGdG6USOTGqNNw55RM1j84mevHZnhEoYJKGw8t/5nZ/97AjqzSVn8ubUlBpdUziN8lhOu/9E5WvoOyDMcL6y+CqtJ+fL7nLL9fttfz+bphfDcebYJI5GZKnyR+XeMiMtldPPzJPk9dy46Cuz4RQEbC+WfLtjVzh3tnS6/YdTaALVFRCW6+2JvLf9cf53RJ+0baqKh0JPIqrJ7bqTHB1+8b2jUWo04Z9t1yoqTDXYMEJVVV4HIBdjBlgy6Kxlh8sqqLOFRxlhhdOEatIjjKMuyWuvGo62qelS7zE4kuFH5mk/5eHtYtZZ52Ax/pn6CXcIal0gQOSEE+SUaW0ZTbCd9ZQtT3BX4i0d6eAgWXJGMbFAc6EZAxoIxRnJUTkBGJ55yJlJIEmtb4/gkQ3hm0UeCsanDNCeIRz+1Dqd0BRWAZeVxmbawJidrftSq84lGUoOzfIGqxSw6srjpqmwkCIIPJWvuxNiQkhaJHHnmEH3/8kaysLPbt28cjjzzC+vXrWbBgASdOnOCJJ57gp59+Iisriy+++ILf/OY3XHDBBQwePBhQIqn69+/P9ddfz969e/n222/585//zJ133ulx+/z2t7/l5MmTPPTQQxw+fJj//ve/fPzxx9x3332edtx///28/vrrvPPOOxw6dIjbb78dk8nEjTfeGJDXRUVFRUUl8OT7dBhSQkAo6psS7bl9OF+t3xVoqqwO3tx4ynN/bI94XrxmKFv/eBF/mt2fHp1qz05qComRBp64YiDf3nsB0/p7OxuH86tY8MY2Nh0vbtH+25OzPsWDO8cG/3etPvqkeN/TowUNd0pU2p4v9uZy30d7PDOEfzMug79c2r/ZEaJ/ntPfM3i08XgxH2w/3VpNDQqyir2d9e6JgReKRmTE0TVemZS26Xix32+yioqKwsq9udyzdDfPfHOEC59dxy3v7mTziWJ1EFlFpYn49fuCUCgyaDWM6hYPKKJWlioMtz3uOtrmM+CoVkSH85BnLmNfWQ4GjY5InfI5qpDD+KtrHv+SLuOkj0DUi1w+0v6Tt/RP01n09tvihGre0z9JF6GEl6RLKJFb1mdsC0STE+OhCqLX5BP9XT6GLBPuq+uz8fDkr0Xs45IID/emcGlxoREUZ9VZORGAOM6dWOdqJaEIELQQ1llR6aQ6xJsaxgteoWhlitcsMuqoTLHexc8Rta8/q+VIzyS0KEF5DnpRi83lxOKy11ofAK0Wytt3ImFICkWFhYX85je/oU+fPlx00UXs2LGDb7/9lmnTpqHX6/nuu++YPn06ffv25YEHHmDu3LmsXLnSs71Go2HVqlVoNBrGjRvHddddx29+8xv+9re/edbp3r07X375JWvWrGHIkCE899xzvPHGG8yYMcOzztVXX82zzz7LY489xtChQ9mzZw/ffPMNycnJqKioqKj8MsmvDO4Ow7n0TfFevB7JVwepA81HO3KosimOtKtHpvPhreO4fGiXJrmHGkNmUiSv/2YkH946lkFdYgAlym3ROzvZGSLOov1nKzy3Q9lR1CvZ5zuoCkUB5Zv9+X4i0XVju/LXywa0qM5ctFGJoHPzzy8PkVPacQZqsmsGnXQaIShmUwuCwK9qajBIsuIOU1FR8VJldfDEqoOe+7IMaw4WMP/1bcx8cQMf7TiN1dE6dXVVVDo6vo6izjHBeS06rqe3FvqWEyUBbMkvhLIyEFxQfQq0EfXGl7kpsVWxpzQbGZk4vTLhplwO5++uKzlKZ896GRTxL81bfG54lDHa/YiCcrFqkY2YZGWCTIpQxnu6f6LHwYuuWTjkwMdZCzYX+hNVRK0rIObrXMIOVKCp9AowZj28c5HI7xdpyIyOJdPqX6rF7SYCr1AUL5wrnAggtmJlHX0MGBIbdBUlCtX0RbnG3BLbH3uY4gIbmCVjsMt8F1s7Pl5CgwnlvQrHjAYnWlGDS5awOOsRisIMUNy+k3lDskbR4sWL630sPT2dH374od7H3WRkZPDVV181uM7kyZPZvbvhbMe77rqLu+6667zHU1FRUVH5ZVDgIxSFQvRcv1Svo+hQnjpIHUgcLom3NmV57t9yQfc2P+bYHgmsuGM8dyzZxZqDBVgcLm58awdLbx3LwBoBKVj5Ym+u5/ak3p0aWDO46eMjFB0rUKPnAkWF2cHDK37GVaMSXTu6K3+7bGCLRCI3F/buxDWj0vlwRw4mu4s/fPIz7988BjHE6oKdiyTJnui59PhwtJrgmIN45fAuvLj2GKDEz916QY9WeR9VVDoCL6w5RmFN1HC/1GjKTHbPJKfD+VX84ZN9PPX1YeaP6cr1Y7uFxKQnFZVAkV/hdbcH63dlfM9EQHE/bD5RzPwxXQPboI5OYSFQDY4KxZnSAFUOC3tKszG77KQYlX5XmRzB312/Ig/FCRZPFbeI33GF5kdSNQWebSVZIFdO5YyUhoiLgZoDhAtWuosFvKt/imvsf+Y9aRI3ada30RNtAKeELs+C/rQJXb4VoQ6zqjVRz6rBEl8OljGFCQyvMnJReW1nukGoQyiq5SiSQWjN+mAiGFOU99Bprrd8ygTxMIelLiAIHE7pxuBTx9C7YOhJmW197Zw22Olq869XXSVHESmYEQSIFKqpkGMBsNQVPQcQpoeK9h2jCY6reRUVFRUVlQ5CfoX3YiYUoucykyJxj1Wq0XOB5at9eZ44tal9k8hMOn9UQWug04j8Z/4wJvVSLr6rbE6uX7wtqGPQThZV8/MZxVE0sEs0PVsYyRdIUmOMRBmUuVuqqy9w/GfdMcrNSiftkgEp/OOKga0q5Pxpdj861wwibT5RwpIOEEFXWGXD6lDiQLoFQX0iNxkJEYzIiAMUl97BPPW3TUUF4EBuBW9vVuJtjTqR164fwYY/TOHf1w5jWNdYz3plZgcvrzvBxKe/5+6luzuUC1JFpTUJ9hpFAAM7R3uuM9U6RW2MwwHl5SBUKcJFA24iq8vOntJsSqzVJBtjEASBUjmCJ1xXekSiRCr5r/YVbtJ97icSVckR/OwayGmpKxIiTnQcdPXDJiuiRH8xmzf0z7JR7sOPUt82fcoeJBltvoXw7cXErjpL5LYS9Hn+IpE9WsvBEWE8f4ORGxZJfDwGTGECyXYtvymMRaD2dfe5jiIdTiJ8liG7ALF1HUWgiEPGZJCsIEt1rjJGOI4GxYG7KnWMZ/moo8qTXluHq6hK9vbvo2oEL40gUuWw1FoXAKMBrPVH4LUFqlCkoqKioqLSihT4Rc8ZGlgzODDqNJ66N8cKqnG66r4QUmlbZFnm9Q0nPfdvmdSjXY9v0Gp47fqRjK7JMS8zO1jwxja/+iPBhK+b6PIhXRpYM/gRBIFeycp38Gy5heqa6EGV9uN0iZl3NmcDYNCKPHpp/1Z3+0SdE0H35FehH0HndhNBcAlFoLiK3KzYpcbPqahIksyjn+33RGvePbUX6fHh6DQilw3pzKd3TOCzOydw+dDOaGvOf05JZuXeXH71301+ca8qKioKvjWKgjVJQqsRGd1dub4vMdk5qrrX247qarBUg1gN2oajCHPNZeSZy0gJi0UUBErkSJ5wzSUfZaJLCqV8qn+csdo9GAQllswli5xyZbDPNRAz/tdddgwccPXDISuCyWjxCP/Vvcg70gVkyU1LXpCQKdO6OGq0sSXKzMr4St5KLuNfacX8oVs+9/fI45m0IpZ0KmOPvRzb/iKivzpL1MYiDKfNCE6vOuQMEzk6yMh/5hu5/naZx6c72JrqRKq5zDZIAr/NiyNMqlue8HcUdSKeavxM4rILRA2IrekoqsHYSakx5az7OxMp2BgqZAGwJXEgzpqo+OEnZDQumR1RFio0/lGuVT61o6IEZYKgTtBS6ainT6DXQjvHwapCkYqKioqKSisSCh2Gc3HXKbK7JE4FqTDQ0dl6spT9Z5VZ7wO7RDO2R3y7tyFMr2HxDSMZnKZEHxRV2VjwxjaPyylYkGXZIxQJAswZkhrgFrWcPim+8XOqq6i9eebbw9hrRPKbJ3Zvs5pXF/TuxLWj0wEw2108tPxnJCl0Z/b6CsndEuuO5QgUcwZ1Rl8Thff5nrPqJAiVXzzLfsph1+lyAHp0imDRpNrxtkPTY3nxmmFsengqd0/NJCFCmZ1eXG3nmte2svlEca1tVFR+ybgdRfEReoy6wNeDqQ/fOkXq97gNqa6GqhLQOEFT/7WkS5I4bSrGqNGjFUWK5Cj+5ppLAbEAJFHBG7oXSRfzPduUSTHscQ0mT06FOpw3AFbCOOjqh7OmNtFUzR6e1L3B/7kuoVpueAJrkdbJ4uQyHs0o4O6eeTzcvYDn0kt4O6WcVQnVbI22cDzMTrlOIqpCYvhOK1d+WMWULypJOWxBY/VeZ9n0cKKPnjfmGrjuboE/z3HyY4YT2WcSVqJdw4zSSB7N7kRne/0ijwFv7Z6zJNaOnZNdgLaVo+dqEHQQlqI4iqS6XT0TBCXW0SlqOZqixDpGWqFvjoxLgHXnuIpsGLDLSlujhGpARq/RYnE6sLuCY7KgKhSpqKioqKi0Ivk+jqKkqNAQivzqFKnRVwHhXDdRoOppRBl1vHvTaI94eLbcwoLXt1Lo87kONAdyKzlZpFx0j+4WT2qQFg9uCr2S1DpFgWLX6TJW/ZwHQEKEntsn92zT4/1xVj+PELXlZAnvb8tu0+O1JVkl3tmPGUHmKIoJ13FRvyRAGeTecEwdGFP55VJqsvPk14c995+4fCAGbf2D2snRRh6Y3ofvH5jMyJoYx2qbkxve3ME3+/PavL0qKqGAJMmeJIlgjxtX6hQpbD5REsCWdHCqqsBWARoadLgU26oosZmI1odTKEfzhGsuRSgT9ZIp52+aD+kjKn1DWYZjrp4ckvpi4/yfMxMRHHb1QZKVvuQVms3cqf2Ml6Xp1DU3SUbmhxgTT2QUsT3aQqHehbMOpUDjkplwQOIf77j49/9cXLVRpnOZ93GHBrb1EXj2SpGbfqfhkSslVvd2IWm8fdoku4aZpZH86XQn/p6dxJUl0XRyNhwZ53YUuWSBfDmOOKEOoUjUgdBGQq0+Dgzx4Kx7jGS4cIqwGjHr65TRnuWja+LndkSdO+FS8LiKtIKLcCwYRC02yYnFZadO9K0cq3ceGnW0Hj1aP/5EEAROnDjR6vtVUVFRUVEJJO4OQ2KkHr02NOZj9En2DlIfzqvksiENF95UaV2OF1bx/eFCALrEhjFrUGAdMrHhet67eQxX/28LJ4tNZJWYuW7xNj66dRxxEfrz76CN+XyPN0bq8qGhHTvnxtdRdER1FLUbsizzjy8Pee7fO603UcY2mJHoQ5RRx9NzB3Pd4m0APPnVYSb3TqJrQnA5chqDr6Ooe5AJRQBXDk/j6/3KbNwVu88ypW9SgFukohIYnvnmsKcG22VDOjMhM/E8WyjEhOt47+Yx3PXBLtYeLsTukrhjyS7+fsUg5o/p2pZNVlEJeopNNpw1I+/BWp/ITd+UKOLCdZSZHWw9WYJLktG0csSuCopQ5Kg6r2hx1lyqxLsJcfzDdSXFKJM2Uynjz5oVDNIcRScocWNFciJFTYyOqySaI1Jv+ohHEQWZhdo1lBPFCtcY5mm2edYr1bp4N6mcQxHeeDe9JNDJoSHRoSXBoSGtQmDgQTtdjljRWf3d2TJQnKplfx8tG/sKnIh2Ytb4q1Epdi0jqowMrw6ji11bZx2ihnDXKCogDida4jkn/UR2gaYt4/5FMKaCowKcllqRgnrBxSjhOD/K/dmcPJD7hI8RZZmxR+GtaTLFOhdVGhdRLu9nokqOIgFFZYsUqqgWk3BITswuGzHU0R9o477JuTRKKMrKymrUztyzX88tjlbX8kDNlFVRUVFRUWkrXJJMYZVyMRMqsXMAfVN9hCLVUdTuvLHhlOf2jRO6odMEXmDsFGVgyS1juOrVLZwps3C0oJrfvLmdJbeMIbqdL1Z9kSSZlXuV2cxaUWDmwJSAtaU1cdcoAjiqCkXtxjf78/kpW+mo9ewUwbWj0tvluBN7JTJ/TFc+2HYai8PFX1ceYPENo9rl2K2Ju0aRVhToHBt8v3kX9u5EfISeUpOd1QfyqbQ6Anr+UlEJBD9ll/HhjhwAIg1a/jy7X5O2D9NrePX6ETz8yT4+2XUGSYY/frqPUpONO6dkquM6Kr9YfOPGU4JcKBJFgXE9E/hqXz5VVicHcisYnBYb6GZ1PEqLwFUF2vrF+GqHlTxLOVZtKi+45lKK0g/vTCl/0qwgRSglVVAmuUiywGmpedemZXIcJ6Qe9NIoBo3faVfwV/l6cuVYUoUytkRZ+KhTBVYfYeeCinDmFkVjlAQ0JTaMx6vRnTUjnONEckXrsGVEYE8PRxuuZSgwtBjkYpkKjcRZg4NyrUQ3q47OzRCH3Ii40AlKHNtZWXlN63QUadr4+6eNAGMKmHNANoDg31efIBzhR7k/Zl0YJzql0aswh7gqmYxCyE6GLIODQWZ/ochNtFBNoZAMgMVZj6PIEIRC0cKFCxt8fM+ePezduxdZlomNjWXYsGEkJytPtKCggD179lBWVoYgCAwZMoQhQ4a0vOUqKioqKipBRkm1DVfNzLJgjyDwpUtsGFEGLVU2J0dUoahdKaqysWK34pCJMmi5up0GqhtDakwYSxaN4df/20JBpY19Zyv4zeLtPDV3EH1Tos+/gzZge1apJ97xwt6dgsLh1Bp0ijR4ZnqqQlH7YHdKPPWNN4rpj7P6oW1HkfaPs/rx/aFC8iutrD1cyNGCKnr7uDuDHVmWya6JnusaH96ur11j0WtFLh2cyjtbsrE5Jb7el8fVo1QXhMovB6dL4s+f7ffcf2B6b5KacX2q04j8a95g4iN0vF4zueXZ1UcpMdl5dHZ/RNWZoPILJM9HKAp2RxHAuJ6JfLVPESC2nChRhaK2IPcUaF0N1icqsFaQ4wzjf/r5lKFMFOtCCX/WrCBGsNBVzEGsUWZy5VTsNN8tUyR3Quty0l2jxBz/SbuE+6WFFKecYF+k10UU6xD5TWEsAyr16HPMGE5UoS33r8kjC+DoHIYtMwpnokEpFHsOAgKxLg2x5taJgfOrT1QjFCWcW6MIAcR2iGYzdgJ7GTirQeffDx4gnCGWasqJZE3KCHoVKpMzRh2VyU4WyDLaGWT2niOqiUCSBURBJlJQ+n2CIGCqTyjStG/9s0Zd0b/11lv1/k2aNImDBw+SlpbGRx99RGFhIWvXruWDDz7ggw8+YO3atRQWFvLRRx+Rnp7OwYMHmThxIm+99VZbPzcVFRUVFZV2xbc+UXIIdBjcCILgcRWdLbdQYam7WKNK6/PelizsTsXGf+2Yrm0ee9VUMhIiWLJoDPE1gsyenHJmvriBe5bu5lSx6Txbtz6f78n13L5saMeJSBQEgV41IkFBpY0Ks/odbGve25rtETrG9UhgajvHkkUatH7F5N/wqVMWChRW2bA4lFiUjCCOzbtyeJrn9opdZxtYU0Wl4/HulmwO5VUC0D81muvHZjR7X6Io8KfZ/XlkZl/Psrc2ZXHfx3s81zEqKr8k8sq9tUdSQqBe5vieCZ7bap2iNsDphOIzoJNBrHsim1NykV1dzDfa6R6RKJ1ij0gUQTWdROW9cchazkot7+vkyalku9KQgdWRRrZ3X+snEo2rDOOvRxMYsd1KzFe5RPxU6icSSQYRS99oKmZ2xjSuE85OxjpForbAXZ8IfB1F5/Y/ZRDaYeKgoIewVMXBJPn300RBZrxwFIAtqQM9y0ceU34bs4zniG6ImFAim8MFK1oc6AUtFQ4zwUCLpn7t3LmT3/72tyQmJrJ161auuuoqtNraSp5Go+Gqq65iy5YtxMfHc8cdd7Bz586WHFpFBVAGVgRBIDY2lvLy8jrXeeqppxAEgccff7xd29aWZGVlIQgCkydP9lu+fv16z2vi/tNqtaSkpHD55Zezbt26wDT4PNT3fIKNt99+O6Q+Sy1p76FDh1iwYAGpqakYDAa6devGXXfdRXFxw8WgV65cyYUXXkh0dDTR0dFMnjyZL7/8spnPIPTwiyAIIUcR4OcQUV1F7YPF7uK9rcoML60ocMP4boFtUD1kJkXx3s2j6VwjfsoyfLE3l4uf/4GHlu/lTFn7XNTanRJf1xTRDtNpmNY/uV2O21741go7Wqh+B9uSCrODf689Bih93T/N7heQ+KRrRnclyqj0nT7bnUuhz2SDYMe3PlG3xOCrT+RmcFoMPTsp7dt2qpSc0uDohKuotDUFlVaeX3PUc//vvxrYKs6/2y7syTPzBuM2EX2+J5db3t2J2e5s8b5VVEKJvMrQchT1SIwgOVpxp+zIKlUF3tamuhqqisBYvwOoxFbNCbuWvaIiuEdj5k81IhHIdBNPe9bNkbrgalwI2Hk5JqRwV6dU/pCUiKnGnBLtFHngUDR3fCWTsiqPsMOViHbvZ8IZp8c0KoGKWV2wDoxFDm8H1845RAne/tCJGtEs3tdRJLsAsX0cRQD6ODAkgLN2P22CeASA4rBYTscqfdQeBZBYIZNlcCDjn9/nGz8XJVSjF7VUO6w4JVcbPoHG0aIrhRdeeAGXy8Uf//hHOnc+v9KZmprKH//4RxwOB88//3xLDq1SD2aHmTJLWVD/mdtAJa2oqFA/Uz4kJyezcOFCFi5cyLx584iNjeWLL77goosu4pVXXgl08wJGqAhSgeb7779n5MiRfPDBB8TGxjJnzhwMBgMvv/wyw4YN48yZM3Vu93//939cdtllbN68mQkTJjB16lS2b9/OnDlz+M9//tPOzyIwFFSGsFDkV6eoMoAt+eWwfNcZymqcI5cO6Uzn2OCdjTigcwzf/34yf57dj4Qad5FLkvl45xmmPLuev3y+v80HuTccK/IU457WP5lwfft3WNqS3ine76Aq1rYt/1l3zOOc/NWwLgzsEhOQdkQatCwYo8zwt7sk3t6cFZB2NAd3fSKAbgnBKxQJguDnKvpst+oqUvll8PcvD1FtU8Sba0enM7xrXKvt+9cj0/nf9SMxaJXhpB+OFnHT2zuQJPk8W6qodBxCqUYRKL+H43sqrgyz3cXPZ8oD26CORkUZVBVCeGS9q5w1l7JBHI1cMxQ/XfyZaEH5HMUJ5cSISh/cIhspkFtnQpxNkHihSxk/RnpTKxYctfLUEiOjPyvFcKoaoUYfkgWwdQ2ncmoyVRelYM+IAE3gokVjhQrP7U3SQAQkYvAZT5YlEDUgtlcih6jUKhJ14LL4PdKNIjpTCsC61GGe5SOPyVRrJUq0/gJQlez9nEQJVehFLXaXE6sr8KkSLephb9iwAYAxY8Y0epuxY8cCsHHjxpYcWqUOzA4zS35eQqmlNNBNaZD4sHgWDF5AuK51YioEQcBgMPDiiy9y3333ERfXehfBoUrfvn15++23PfdlWeZvf/sbjz/+OA888ABz584lKal9I1ZUQgOz2cz8+fMxm8089thj/PWvfwWUz9BDDz3Es88+y80338y3337rt92RI0f4/e9/j8FgYN26dYwbNw6Ao0ePMn78eO677z4uueQSMjMz2/05tSehGj0H/o6iQ3nqIHVb45JkFvtETflGUAUrRp2GRZN6cO3orry16RT/+/EkVVYnDpfMO1uy+WhnDgvHdeO3F/Zsk9pBvrFzl3eg2Dk3vZO8HQa1TlHbcbrEzDubFSefQSvy++l9AtqeG8Z3Y/HGkzhcMu9vzebOKZlEGIJfBM0q8XbUgzl6DuCKYV3417fKTM9Pd5/lrqmZAXGQqai0FxuPFbNyr/KbGReu46EZfc+zRdOZ1j+Zd28azaJ3dlJlc7L1ZCmr9uVx2ZCO9/usolIXoVajCJSo3U9rJkxsPlHCyG7xAW5RB6L0DNjMYKj7HFjtsHLKYmK7OBwAHU4uEvbVPCqT4eMmOi2le8SkluBC5vWUMk4bHQiSzKQjMos22zEWagHvxFBZK2DrEYk1MyogzqG60OAkssY9dFzqTAHxxFKNVvBxwslOQANCO7ZZGwnGZDCfAY0RUK4nBUFxFS2TxrE1ZQALD30DKELRNyOV+LnEam87K30dRVSjEzXYZScWl51IXWDPJy365BUVFQFgs9nOs6YX97rubVVaD5vTRqmllDBtGPFh8UH5F6YNo9RSis3Z+M/M+RBFkVtvvZXKykqeffbZVttvR0IQBB599FF69uyJxWJh9erVgW6SSpCyYsUKCgoK6NOnD3/5y188ywVB4J///CfdunVj9erV7N2712+7F198EZfLxW9/+1uPSATQu3dv/vSnP+F0OnnxxRfb7XkEivwK77kt1BxFfVJUR1F7suZggWegdUJmAgM6B8bR0BwiDFrumtqLjQ9N5c4pPQnXKxkGVofE/348yQXPrGP1gfxWPabZ7mTNwQIAYsJ0TOrVqVX3Hwz09o2eU4WiNuPpbw9jdymdzEWTugfcyZcSY+SyIV0AqLQ6+XhnTkDb01h8o+e6B3H0HECX2DDG9VBqM5wsNrEnpzywDVJRaUNsThePfb7fc/+Rmf3aZPIGwJgeCbw03ztz+vnVR3C41DgrlV8GbkdRTJguZFzu4/zqFDUcKa/SREpzlNo19dQnyreU84M0AKugRNNNFA7XRM5BslBIeM3tSjmSErnlAp6MzEedKtgXaWPYcYkXX3Nx12dSjUik4AjTYB4US/nsLlgGxwWNSAQQI1R6SiH9IA0GIJ5z6hPJLsXd055CEYAhCTTh4PRPyxovKJOSsqJTKAlTJuH2PisjSjJZRrvfug70WGXlsxIpVKMRBWRZxuL0Xy8QtEgo6tRJ6aR//fXXjd7mq6++AiAxMbElh1ZpgDBdGJH6yKD8C9O1TWf84YcfJiwsjJdeeomSksYX5pNlmaVLlzJ16lTi4uIwGo3069ePxx9/HLO5dkRet27d6p2B6K4PdMMNN/gtv+GGGxAEgfXr1/Ptt98yZcoUYmNjEQTBU1dpw4YN3HXXXQwePJi4uDjCwsLo27cvDz/8cL21l5qKKIoMGTIEgJycnFptzs/PZ9GiRaSlpaHVavm///s/z7Zbtmzh8ssvp1OnTp56NXfccQe5ubm1juNbF+fIkSPMnTuXhIQEIiIimDBhguccUB8Wi4WHH36YjIwMDAYDmZmZPP3008hy3VEGpaWlPPLII/Tv35+wsDBiYmKYOnUqq1at8lvv8ccfp3t3Zcb+Dz/84FfH6dz3rDk05bM0ePBgBEHg8OHDde6rpKQEvV5PcnIyTqd/3ve2bdu46qqrSE1NRa/Xk5aWxqJFizh9+nSd+2oqP/30EwAXXHABouj/E6HT6ZgwYQIAn3/+ud9j7jpE8+bNq7VP97KVK1e2ShuDmVCOnos0aEmPV87RR/Kr1PiQNsa3cP0tk3oEsCXNJyZcx4Mz+vLjQ1O4eWJ39DURNFU2J7cv2cUnP9UdU9kc1hwswOJQLPuzBqV6jtWRiIvQ0ylK6TweLag+z9oqzeGn7DK+/Fmpc5UYqef2ycHhcr31Au85YPHGUzhDYKDVLXRrRYEuQRyb6eZXw7t4bq/YpcbPqXRcPt6Rw8kaIXdERhzzRqSdZ4uWcWHvToztoQxqZpWYWbaz9X77VVSCFVmWPUJRqLiJANLjwz39vV3Z5Vgdga+H0mEoyAaNBuoYL3RKLrKqS9mkGetZNlPcA4CIi3TRe97MdmXgdqm0hDWxJrZEmLj5GxePLJNIKfM+ZohxkDqmjC6zy7H0jgRd8PWrYoVyz+0NNUJRnHBO/0h2gab+mlBthqhXahVJ/gaIZKGSXuSBILA/Xrm2NzogrRiyDLUj5dx1ijSCRHhNpJ6pFU0VzaVFn4apU6ciyzLPP/88mzZtOu/6mzdv5oUXXkAQBC666KKWHFpFxY/U1FR++9vfUlVVxb/+9a9GbSNJEgsWLGD+/Pns2LGDoUOHMmvWLEwmE3/961+ZMmUKFovl/DtqJB988AEzZ87EZDIxc+ZMRo0a5RGdHnzwQRYvXkxYWBgXXXQRF110EZWVlTz99NNMnDiR6urWGTCqqlJmKBsM/ifToqIiRo0axZdffsm4ceOYOXMm4eFKjMj777/PpEmT+OKLL+jTpw9XXnklBoOBV155heHDh9crdpw4cYIxY8awe/dupk+fzsiRI9myZQtz5szhrbfeqnMbu93O9OnTef311xk5ciRTpkzh7NmzPPzwwzz66KO11j969ChDhw7lqaeewmKxMGPGDEaOHMm2bdu49NJL/RxmQ4cOZe7cuYB/DaeFCxcyceLEpr+YPjT1s7RgwQIAlixZUuf+li1bhsPh4Oqrr0ar9c6O+O9//8v48eNZsWIFGRkZXHHFFSQkJLB48WJGjhzJoUOHWvQ8AEwmpWNZX4RjQoIyC8nXUVReXu4RqoYNG1Zrm/T0dBITE8nOzqaysmM7VdzRc0adSHRY8MzIaSzu+Dmz3cWZstY7/6n481N2GTuzlav13smRXNg7tN0xiZEGHp3Tnx8enMysQSmAEq33wLK9vLnxVKsc44sOHjvnpk+Nq6jUZKe4OvAdhY6ELMv848uDnvv3XtybyCCJeOuTEuU5D5wps/D1/tZ15LU2siyTXVOjKD0+HK0m+AYYzmXmwBSMNQMhK3/OVYt4q3RYdvs45h6Z2RdRbNuYRUEQeOgSb7Tdi2uPqoPPKh2eUpPd404OhfpEvozvoUzat7skfsouO8/aKo1CliDvOITV7bAutlWxwZlGiaCMsQwSskkTlJIhXcRc9IIiIpRI8VQRVec+msJPkRZ2OSt4+k0XM3Z7J386Eg1UTexE/rROxHa3kKCtIkysaGBPgcNdn8gli2yTlN+YeM4ViqSa+LcAoIsGQaO4yHyYICquoiNx6Z5lmbkyp40OJPwn4ppk7+clQjChFbRUOgI/BtOiq/qHH34Yg8GAzWbjoosu4t5772XPnj1+M/9lWWbPnj3cd999TJ06FavVil6v5+GHH25x41VUfPnDH/5AeHg4//nPfxoVbfjcc8+xdOlSJk+ezLFjx1i3bh0rVqzg+PHj3HzzzWzfvt1Tn6U1eP3111m6dCnbt2/3/I+JUaKG/vKXv5Cfn8+2bdtYvnw5q1at4tSpU9x6660cOHCA559/vsXHLywsZNu2bYDiaPHlq6++YtSoUZw6dYply5axcuVKbr31VnJycrj11lsBxUGyceNGli5dyqFDh7j33nspKCjg+uuvr/N477//PpdffjlHjx5l6dKl/PDDD3zxxReIoshdd93F2bO1Z3Nu2bIFjUbDqVOn+OSTT/jmm2/YsGEDGo2GF154wU8wc7lczJs3j5ycHJ555hlOnDjBZ599xtq1a9m7dy/du3fn4YcfZv9+JXrhiiuu8AhH7hpO7r9Fixa16LVt6mdp/vz5CILA0qVL69yfW0ByC0oAW7du5Z577iE1NZXt27ezdetWPv74Y/bu3csbb7xBUVERN954Y4ueB3idotnZ2XU+furUqVqPu0WiuLg4IiLqvjhKS0trcL8dhYKamWUp0caQrH/Qzyd+7pAaP9dmvOFXm6hHSH5W6iI1Joz/XDucheMyPMv+tuogL6w5Wq8rtDGUmez8cFT5XU+JNjK6A+ep+8XP5avxc63J1/vz2XW6HIDMpEiuGZXe8AbtjK+r6LUfT7boO9PWFFXZMNuVgeBgr0/kJsqoY8YARcguNztYd6QwwC1SUWkbzpR6B5n6pUY3sGbrMbxrHBf3UwqvF1TaeHdLVrscV0UlUIRifSI34zPV+LlWx1wIJcUQHlnnw2fNpfwojvHcnyXsAUCHnc6C4nSXZIFsqeXXpsf1VopPlvCPd110qSlfL2sETMPjqL4wCWdKGIVyKpWycv02QnMEPbXTlAKJAStGQZkwlyt3woLyHYsXzomeQwZB186tq0EbAZoIcFn9Fo8VjiEicTTWRyjKk7GJMnl6/7QgE75CkRm9RkOlw4IkB3YyU4uEor59+/LOO++g0Wiw2+289NJLjBgxgvDwcLp06UJaWhrh4eGMGDGCf//739jtdrRaLW+99RZ9+7Z+QUWVXzbJycncfvvtmEwmnn766QbXdTqdPPPMM0RERPDhhx+SkpLieUyv1/PSSy+RkpLCa6+9hiS1zpd09uzZXH311XU+NnPmTI9o5MZgMPB///d/aLXaWjFfTcFqtbJt2zYuu+wyKisr6dOnD1OmTKl1rJdeegmj0f8i54033sBisfDrX/+ayy67zLNcFEWeeuopOnfuzM6dO+t0FEZGRnra72bOnDnMmzcPs9lcp6tIFEX+97//ER3t7dSMHDmSmTNnYjab2blzp2f5ypUr2bdvH3PnzuXBBx/0i0nLzMzkueeew+Vy8frrrzfh1Wo6zfkspaenc8EFF3DixAm2bt3qt7/s7Gw2bdpEZmYmY8Z4LyaeeuopXC4Xr776KiNGjPDb5uabb+ayyy5j27Zt7N69u0XP54ILLgCUKLniYv8Lx7Nnz7JmzRrA604DPAKe24VWF24ByXe7jobJ5qTKpvz4J4dY7Jybvj4DCofzOu57FUiyS0x8W1O/p1OUocO5Y0RR4PHLBnDPRb08y15ce4y/rjzY7DjDr/fn46zZ9tIhqW0+OzqQ9E72djDVOkWth0uSefobrwP6j7P6Bp0LZnzPBPrXnIP3na1g26nSALeoftyxcwDdEoK7PpEvvxrmjZ9bfaAggC1RUWk7zpQp38/4CD0R7eiafHBGH0/i0n/Xn6DSWjtmR0Wlo5Bf4Rs3Hvzxq764a/YBbDnR+LINKg1Qkg1WE4TVFoqqHVa2mo2cEpWJdF0oZbCgTJ5NF8+gEZQxogI5CSst+yyV2qxE/1jENT9KaGuGMZ1xOiovTsHeI8oTi5ckmPjEeaFnuwxNNiLB47R2u4kAjkhdPbfjznUUgVKjKCCIYIgH2f+3LlqwMFjI5nhsF1w1EYKZuUo/Nsvov65J9o6fRQgmDKIWu+TA6grs72eLe0i//vWv2bRpEyNGjECWZWRZxmazkZeXR25uLjabzbN8+PDhbNy4kWuuuaY12q6iUos//OEPRERE8Morr1BQUH8HcNeuXRQXFzN+/HiSk5NrPR4WFsaIESMoKyvj2LFjrdI2X6GlLs6ePcurr77Kvffey0033cQNN9zA7bffjl6vb3IbfGvwhIWFMXbsWLZt20ZmZiafffYZGo3Gb/3hw4fTpUuXWvvZsGED4O9scWMwGLjqqqv81vNl+vTpdcaXXXvttfVuk5GRQZ8+fWot7927NwB5eXmeZatXrwbgyiuvrLU+wKRJkwDYvn17nY+3Fs39LLlf0w8++MBv/Q8++ABZlv1ec0mSWLt2LeHh4cyYMaPOdrTW850+fTrDhw+nurqamTNnsn37dqqrq9myZQszZ8701Ew6t36Rijd2DkIvgsBNXx9H0WHVUdQmvLnxFG695Ibx3TBoNQ1vEIIIgsD903rz6Jz+nmVvb87i98v2NqvI9ed7vA7Uy4fW/q3qSPT2+Q4eUesUtRr7z1aQXSNujO0Rz5Q+SQFuUW0EQeC2C72uotd/PNnA2oElq9g7o7NbiDiKAMb2SECnUTrtu3PUuB2VjofdKXmuR9Pi2nfwuk9KFFfU/EaXmx28saF1omdVVIKRvMrQdRQlRRvJTFIEjb1nKqi2Oc+zhcp5KToFNicY9bUeyreUs4aRnvszxd0IAoRhJllQ3M1OWUOO1IJ6crKMK6eKzt8W0ueM0tGUBDD3jaZqSgpSVG0xJQE7OyRl7K2TUEGCEDwTaHzrE+2SentuJ/gKRbIEiAEUilDi50QdSHa/xROEI9i0BrKjlUnk6UVgsMtkGfzXc6LDJiufmQjM6EUNNpcTi8t/vfamVaaYjBo1ih07drBz506+++479u3bR2mpMgsuLi6OQYMGcfHFFzNq1KjWOJyKSr106tSJO++8k2eeeYannnqKF154oc71srKyAFizZs15I3+Ki4vrFC+aSteuXet97Pnnn+fhhx/G4Wgd5Tg5OZlLLrkEAK1WS0JCAmPHjmXOnDnodLVPpPW1LTdXqQnRrVu3Oh93L68rRi4jI6PWMt9t3Pv2xR1Pdi5RUcrAmc3mrdfgfg8XLFhQp5Dl5lxXTEP8/ve/r7X+xIkTG4yma+5nad68edx999189NFHvPDCCx7xrq7YueLiYo9rR6+vffFx7jEa4rPPPuOzzz6rtfztt98GlIGqFStWMHv2bHbu3OnnakpOTubxxx/nz3/+s58IGBmpXGiazfVblt21j9zvZUekwG9mWWh1GNxkJERg1IlYHRKH1dirVkeSZL7Yq5z7wnQaFoyp/3ehI3DzxO5EG7X84ZOfkWRYsfsslVYn/5k/DKOucQJZXoWF7VnKNWWPxAgGdG6fGJ1A0StJdRS1Bb4Z/LMHdw7auMdZg1J5+uvD5FZYWXu4kOOFVWQmBd/v5qkSH6EoMXQcRUadhgGdY9iTU87JIhPlZjux4Q1fV6mohBL5FVbPZJT2FooA7ru4Nyv35uKUZBZvOMnCcRkkRAag0LiKShuTX+GNeAzFCYLjeyZwvLAalySz41QpU/oG3wSakEGWoeQ4SFrQ+Q+xOyUXu6tt/CwOACAKCxMFxeGeIZ72uDDPSp1x0jzBQ7BLGHeXYMzxfiZLo0Ea0QlNQv2/A4mCia+d4xmsO4FBcNJDPI3JFY2ZQF/XycQIyoRVh6zlZ9k7iSpO8BWKXCBqQAxgvVFNGGijwF4BPuN0I4STGLBzJK4rPSrz0MjQrQCyOtUe7zXJERgEO1rBRYTopECWsDjtEMCfzlZ9RUeOHMnIkSPPv6KKShvy4IMP8t///pdXX32Vhx56qM513BFgmZmZTJgwocH9JSQkNPj4ufusj3Nj3dxs3bqVBx54gJiYGF588UUmT55MSkoKBoNyZujcubOfk6YxuGvwNJb62nY+WnugpSkuFffrfckll9Tp5HGTmJjY6H0uX768zho6DQlFzf0sxcXFMWvWLD799FO+++47ZsyYwd69ezlw4ACjRo2iVy9vbJP7GJGRkcydO7fBYwwYMKDBx/fs2cM777xTa7nv5yUjI4M9e/bw6aefsnnzZiwWCwMGDGDBggWsWLGi1nHcQmNZWRkmk6nOOkVnzpzx7Luj4usoCtXoOY0o0Cc5ir1nKsgqMWG2OwnXB0ex947A8aJqyszKBeKEzMRfxADlVSPTiQ7TcfcHu7G7JL47VMANb23n9d+MJMp4/g7Rqr15uEu1XDY0eAf4W4soo44usWGcLbdwtKAKWZY7/HNuD3467RWKRnSt7XYOFnQakZsmdufvXx4C4PUfT/H0vMHn2ar98Y2q6ZUcfEJWQwzrGsuenHIAdueUB6W7TEWlubhj5wDS4trf7dc1IZxrRqfz/tbTmOwuXl53gscu7X/+DVVUQoxQrlEEilD07hZl3GPziWJVKGoJ9jIoKwCx9sh+ka2Sr1wDkWomBV8k7EMvuIgWKogXywGwyXry5JRa2zYKl0TkDwVoK7wCxJb+Iil9k4lthNNmjHCK15xzuFv3GRpBpqt4msNSXyBwfY9IqtEKSh3MCjmGUrzXmfH41CiSnYAmcDWKABBAHwf2UkDG/boZBSfDhCyOxqUzM1upE5+ZJ/N1mgOHIKOTva+viXDiUfopkYIJELAEOHpOHf1R6XAkJiZy99138+STT/Lkk0/SuXPt+g9u50pTBRW3m6O6utrjonCTk5PTrPZ++umnAPzjH/9g4cKFfo9ZLBby8/Obtd/WoHPnzhw5coTs7Ow6BQi3m6au2Lq6BBff5XW9L03B/R4uWrTovOJJY3E/n+a0o6mfJVBcQ59++ilLlixhxowZHjfRdddd57deYmIiRqMRURR56623WjRo+Pjjj/P444+fdz2tVstVV13liRd0s3nzZgAmT57sWRYbG0vXrl05ffo0u3fvZuLEiX7b5OTkUFxcTEZGhl/9qY5GR4ieAyU6ZO+ZCmQZjhZUMzQ9NtBN6jD41hwZ0z0+gC1pX2YMSOHtG0dxy7s7MdldbD1ZyvzXt7H4hpEkRTX8Xfl8r9exetmQjlXPqT56JUdyttxCldVJfqWV1JjQyr4PRnbVOIoi9Br6pAS3sHH1qHRe/O4YVTYnn+4+ywMzep/3e9KeFFZaPUJL35QousSG1udzWNc43tqUBcDu06pQpNKxOFPmnVEeCEcRwD1Te7H8pzNYHRLvb83m5kndQ+48oaJyPvxqFIVgv29M9wQEQTHDbFbrFLUMWxGUl4G2tlB0orqKreJwADS4mCb+DMh0E0971jktpSHRvCjysIOVHpGo2ghvT9dwQWQSsfbGiSdRgpV8KYWjUhd6i2eJFytIlfPJk1Ob1Z7WwLc+UbkcQ4msjLuGYcMo+AgosqTEvgkBljV00YpI6LIqDqMaxgjH+SJuqOd+Zq6MJECOwUEPq3eyqEn2TrIOF8yIQhRVDu9veSBQi0yodEgeeOABoqKieO211+qMRRs1ahQxMTH88MMPnpjExpCaqpwwjx49WuuxNWvWNKutZWXK4EVdsWvLli1DlptX/Ls1cNe9Wbp0aa3H7HY7y5Yt81vPl9WrV1NeXl5r+YcffghQS0xoKtOmTQO8QltjcAt97jo7rUFzP0sAc+bMISYmhs8++wyTycTSpUvRaDRcffXVfutptVomT55MZWUla9eubbW2N5X8/HyWL19OQkJCrdpQs2fPBhRX1rm4l1166aVt38gA4hs9F6qOIoC+KV4x73CeWqeoNdnuIxSN/gUJRQDjMxNZcstYYsOVjsu+sxVMfHodv1+2l31nKurc5kRRNfvPKp/BQV1i6NGpdoHYjkgfH4fGUbVOUYvJLbd4Zv4O7RqLRgxuh1aUUcf8sYpT1+6SeHdz3RNvAsWaQ94M+2n963d0ByvDu8Z6bu8+rdYpUulY+DuKAiPOJEUbuWF8d0A5h734Xe1+s4pKqOMWiiIN2kY55IONuAg9/Wr6fAfzKikzBbYmSkhjKYBSExj9haIqh4XPbV2xCMq5eIJwhDjBTIJQWuMcAZMcTpHcqVmH1ZTaMBytiWjTwOMLNAyNTSC9kSKRm2nCPv7p8E5UThPPYMDawBZtS6zo7ReWSTGU1UThxXNOn0h2gSYIxlxEA+hjFKHIhyFCFnlR8Vg1yvuRmaeM655bp8hXKIrEhF7QUumov6RDe9DqQlFWVhY7d+5kw4YN/Pjjjw3+qai0FQkJCdxzzz3YbDYWL15c63GDwcBDDz1EVVUVV155JSdP1i4YfPbsWd577z2/ZRdeeCEATz75JC6Xy7N86dKldYopjaF3b6U42+LFi/1qFB08eJA//OEPzdpna3HzzTcTFhbGhx9+yJdffulZLkkSf/zjHzl79iwjRoyoM3Kturqa+++/30+U+frrr/n4448JCwvjxhtvbFHb5s6dS//+/VmyZAlPPPGEX/0iAFmW2bRpE5s2bfIsS0xMRKfTceLECb/3ryU097Pk3nbevHlUVVXx+9//njNnznDxxRfXGaX3pz/9CVEUufHGG1m/fn2tx6urq3nzzTexWFo++2D//v1Yrf4/dGfOnOHyyy+nqqqK5557jrAw/87n7373OzQaDa+++ipbt271LD927Bj/+Mc/0Gq1/O53v2tx24KZjuIo6pvqHaRW6xS1HrIss/2UMmMvXK/p8LV26mJoeizLbhtHcrTSkbI7JZb/dIZL/7ORK/+7ic/3nMXu9Ma4frHHW8vu8qG/DDcRQG9foUj9DrYY3/pEwRw758uN47ujrRG03tuajdkePIWm1xz0CkXT+zczKiWAdIkNo1OUcg7ac7ocSQrchCwVldbG11GUHoDoOTe3X9iTKKMyy3v5T2c4XqhOelDpOMiyTG5NjaJQ7vON76nE4ssybDuluoqahSxD5QmolsDgL9DkmStYh7c0y0xxDwISXUVvElG2lE6zYt4kmfCdpQg1lzDLJ4iMFmIYYG7651EvuOglFPGe82IAdIKLPppjCDRcWqMt0OAkCqXvY5aNlBGNvaZ2U7xg8l85WIQiAF1N/0L2vmZGwckgzRmOxyqGgORyiDLLZBn9Y+Vs6HHIiqMsQjCh12ixOB3YXYG79m8VoejIkSMsXLiQuLg4evbsyZgxY5g8eTJTpkyp92/q1KmtcWgVlXp54IEHiI6Ornfg/OGHH+b666/nhx9+oF+/fowdO5Zrr72WuXPnMnDgQNLT03nuuef8trnzzjvp1KkTy5cvp3///lx11VUMHTqU66+/vtmD4DfeeCMpKSmsXLmSPn36cPXVVzNt2jSGDh3KpEmTAlrTpWvXrvzvf/9DkiQuvfRSJk2axPz58+nfvz/PPfccycnJvP/++3Vu665n07t3b6699lomT57M7Nmzcblc/Pvf/67TQdUUtFotn332Gd27d+exxx6ja9euTJs2jQULFjBjxgxSUlKYOHEiO3bs8Gyj1+u55JJLyM/PZ8iQIfzmN79h0aJFvPXWWy1qS3M+S24WLFgAwKuvvgrUjp1zM3HiRF5++WXy8vKYMmUKgwYNYu7cuVxzzTWMHTuWxMREbr755lqCWXN49tlnSU5OZsqUKcyfP5+LL76YzMxMtm/fzqOPPlorIhGgT58+/Otf/8JmszFp0iRmzZrFFVdcwZAhQygpKeH5558nMzOzxW0LZvIrlddeECApKnQL9/o5ivJVR1FrcbrUTEHNZ2RERhxazS/T1N0rOYqVd01k0cTuRBu9UQG7Tpfzuw/3MOHp73lhzVEKK618sVcRigQB5gz+ZQpFRwpUoail+ApFwzNCQyhKiTFyWY04WmFx8PGO5sUbtzbVNiebjyuDSakxRgZ2CT3BWxAEj6uoyubkeJE6gK3ScfAViroEyFEEEBOu47cX9gRAkuH5NUcC1hYVldamwuLA6lAGhEOxPpGb8Zne+slq/FwzcVRC2Vlwav2EIqfkYqUpmmJRqZfdX8ghQygmSSgkTFAml1ZI0ZTLsc06rPFQBdpKRXA4lQw/DdMxtbx2nejGcoFwiHedl3BCUhKUIgUT3cT2d7RHC5W4qyxUyF43EUDcuY4i5ADXJ/JBF6WIVue4ikYLxzkSm+65n5lbWygCAXONq0gvOIgQZeySE4srcC6/Fo9SfPbZZwwfPpz333+fiooKZFlu9J9K22BxWKi2Vwfln6Udsxbj4uK49957631cFEXeffddPv/8c6ZNm8apU6f45JNP2LhxI0ajkQcffJA333zTb5vk5GR+/PFH5syZQ15eHl9//TUxMTGsWbOGyy67rFntTEhIYMeOHcyfPx+73c4XX3zB2bNneeKJJ5rtUmpNrr/+ejZs2MCcOXM4dOgQy5cvx2KxcPvtt/PTTz/Rt2/fOrfLzMxky5YtDB48mG+//Zbt27czduxYVq5cyaJFi1qlbb169WL37t38/e9/Jy0tja1bt7JixQqOHj3KsGHDePnll2sJL2+88QbXX389JSUlfPDBByxevJgffvihRe1ozmfJzYUXXugRzcLDw7niiivqPc5vf/tbdu7cycKFC6mqqmLVqlV8++23VFdXs2DBAlatWkVMTEyLngvAFVdcwbhx4zh8+DDLly9n3759XHLJJaxbt46//e1v9W5333338cUXXzBu3Dg2bNjA2rVrGTlyJCtXruTuu+9ucbuCHXf0XEKEAV0IiwDxEXqP4+NwfpX6e91K+NYnGt3tlxU7dy5J0Ub+PKc/W/94Ef/81SC/qLWiKhsvrj3GuKe+51SxMnNsTPf4kJ6x2VQykyI9naRjqlDUYnb5xIsNCxFHEcCtF/Tw3F686RROV/vP7DyXH44UYa9px8X9kltUMzGQ+H4O1Pg5lY5ETk30XEKEnnB9YOs23DihG4mRyvXkV/vy642ZVVEJNfJ86xOFcNz4qG7xnjjeLapQ1DyshVBRAk4BDN66M0XWSr6ShnnuzxT2IOIiXfSWxciWutIcN5Gm3I7xsDKZ0ynCf2druLokFk1znEnufQoys8Xd3On4HVZZEV9SxQIShOJm77M5+NcniqVU9vYRa0XPIYAY4PpEbgQt6ONA8p+0PUzI4nicd4J8Zp5Mgd6JWfS/pjf5CGKxGit2yYnZ1fIJ4M2lRa9qTk4O1113HRaLhS5duvDggw8SHh7OrbfeiiAIfPfdd5SWlrJz507ee+89cnNzmThxIo8//jgaTfOKdanUj0FrID4snlJLKRZnYItfNUR8WDyGOgq9NYfzDWD+9a9/5a9//WuD61x22WVNEnn69u3LypUrG92et99+m7fffrvBfaalpbFkyZI6H8vKyqq1rFu3bnUea/LkyU0a1G3s+uPHj+eLL75o9H7d9OvXj88+++y869X3fNw8/vjjPP7443U+FhMTw5/+9Cf+9Kc/NapNSUlJvPvuu41a91xuuOEGbrjhhnofb+pnCRSRKSen8TOFhw4det7Pk5vztbc+rrjiigYFq4a49NJLO3wtorpwSTJF1cqPeUpM6LqJ3PRNiaagsohys4OCStsvapC+rdjxC65PVB/hei3zx3Tl2tHpbD1Zyrtbslh9sACXJOPyiYO6fGiXALay/QnTa+gaH052iZmjBdVIkowY5HV1ghWL3cXBXKUz3Ts5kpiwIJl52Aj6pkRzQe9O/Hi0iJxSC98eKGD24MAVFwZYczDfc3v6gNCrT+RmWHqs5/bu0+VcPapr4BqjotJK2J2SJwY5UPWJfAnXa7l7aiZ/+eIAAP9afYR3bxod4FapqLScfB+hKDU28N+15hJl1DE4LYbdp8s5VlhNYZWVpCi1z9ckrAVgsoFDAh9xfrtFzwlRqdWWQhnDhFN0FvLQC4qbpFiKp5pm1F6VZMJ3lngi5z4bJ5AaFk5mZcvHH0YKJ/hIGsdfnDfwtO51AHqKpzC5IrDSPp9zt1AkyQIVcjTZJHoeS/IRkZSINwHEILqu18WCJR8kF4iK3hEu2BHjvdpHz5pU9WyDg34W73tWLXujYqMEMxCGxRk4R1GLhKJ///vfmM1moqKi2LZtG507d+bAgQOex6dMmQIotUQee+wxbr75Zj766CMWL15c76C4SvMJ14WzYPACbM7AKY+NwaA1EK4LXGayioqKSmtTXG3zDGyH8swyN31To/jhaBEAh/IrVaGoFdiepQhFeo3IEJ9BShUlCmpczwTG9Uwgt9zC+1uz+XBHDqUmO6kxRmYNDOzgeCDonRxFdokZi8PF2XIL6fHqdVNz+PlMOc6ac/OIEImd8+XWST34seZc/MoPx5kxIDlgsZUOl8T3hwsBiDJoGdM94TxbBC+D02LRiAIuSfZznKmohDJ5FRbc8+7SAlifyJdrR3fl9Q0nOVNm4cejRWw9WcLYHqF77lBRAX9HUShHz4FSp2j36XJAcRX90iZntRhTNthqT+ZaZu/nuT1T3INecNBFVFQCWYbTUnqtbRqD8Wgl2nJFbDqdCF+N0fDY2daJARYFmCPu5nXXVMaIh7hSsxFtTb2ifa6BSK1TuaZeDFg9sXxVRCKh4YjsjR7vLeR5V5ZdIGiCJ3oOQBcJ2nCQzCB6nVC9I/Kp0EcQYzeRmSeDLJNltPsJRe7oOVDqFAlCOKYACkUteqe/++47BEHgjjvuoHPnhrPjw8LCeP/99xk2bBgffvghn3zySUsOrVIP4bpw4sLigvpPFYlUVFQ6Gr4zy5I7gFDUz7dOUZ4afdVS8iusZJcocTBD02Mx6lRXdX10jg3joUv6svnhqXx82zg+vWMCMeFB1AloJ3zj+I7kq9/B5vKTjwgwPIRi59xMyEygf6pyPt5/tpLHVx4IWBzojlOlVFqVwrqT+yah14ZuxGqYXkO/VOU7dqywmkrruXnxKiqhh299omBwFAHotSL3Xdzbc/+Zbw6rkcYqIU9+hfe7FuqT6cb39Do21Pi5JuKoUhxFFg34RPGecWjYIvUBIAIrk4RDpIln0QhK3FiBnNQsh45Y6cB4sMZxI8ArszXMrIwmxtV6/coJwmFiMfNnx00cl5Qx/gjBTDcxq9WOUR8xPo6hCikGSYajsjJZMAoLqZR7V5ZdIIjB5ShCBH08SP7XlMPFLI7GKcJgtAWSyqlVp8hMGC5Z+QxFCGZ0gpZKh7ldWl0XLbrCd0dijR8/3rPMN6va6XT6H0wUueeee5Blud56HSoqKioqKqGGO+oDOoajqE+Kd5D6cH5lAFvSMXC7iUCNnWssRp2G0b+w2kS+9Er2xlEcUesUNZtd2T5CUQg6igRB4G+XD0CnUfpX7289zeKNpwLSltUHCzy3p/cP3dg5N27hUJZhb055YBujotIKnCnzDioFi1AEcMWwLvRKUn7Tdp0uZ7M6GK0S4nQkR9GIjDj0NU5l9bvZRGxF4KiEShcYvILFO9VpuARFvJkq7CdGqCZFUK6hXLJIjpRW5+4aRJaJ2FlCjdbEytEClgQdU8ojGt6uiegFFzPEvZgxcofjd9hkJYQsRSwksY3rFfnXJ4ohl3hMKN+v3kIufmUxZZciEglBNvlSF6PUK/IRiyIFG+Vx3km4mXkyWYZz3UICZhRDRZhgJUwUqHJYcUqu9mh1LVokFJlMSpHh9HSvbS483OsWqaioXbBwwIABAOzdu7clh1ZRUQlSbrjhBmRZrremkIpKR6TARyhKDvEOA0DPTpFoa2qiqI6ilrP9lLfjpQpFKo3BV6w9pgpFzUKWZX6qEYpiw3X0SGzdznR7MbJbPM/MG+y5/4+vDrH6QH4DW7Q+siyzpkYo0mkEJvfp1K7HbwuGdY313HbH7qiohDL+jqLgSfDQiAJ3X9TLc/+rfXkNrK2iEvz4ThBMjQ4eUbY5GHUaz+/h6VIzOaWBczGEHNZCcDmh3AxGPaBMPvnMmgGABhfTxZ/pKuYg1hQVypVTcaBv8qEMx6rQliriQm48fDxJ5JqiGDS0fg3Ti4R9GLFzVE7nMeeNnuU9xZOEYWlgy5YgEyMok1OdsoZqIjkie6PH+wi556zuAtEAbfD8W4Q2HHQR4PJ/ncLiJc/tzFyZcp1EucZfBDL5xM8laBw4XE6srsA43lskFMXExABgtXpPlAkJ3szZEydO1NrGLR4VF7etGqmioqKiEhw4XRJVVgfF1TYqLHbsTleHi53wjZ7rCI4ivVYks2b254miauxO6TxbqDTE9lOKo0gUQtPVoNL+dE+MQFMj1h4pqA5wa0KTU8UmysxKB2tE1zi/1INQ41fD0rinZqBVluF3H+5h35naE/LaioN5lZwtVzq9Y3skEGUMpqiP5uEbRbhbrVOk0gHwFYrS44Nr8PrifkkYauIq1xwsQJI6Vj9A5ZeF21EUptMQHdaisu9BgW/8nLvPotII3PWJbHaPo+iAM4oiWRHqBwo5dBUK6CQqEwYdspZcqel1V8UqB2H7ayLnUCLnhlrD6eNT46Y1iRRsTBX2A/CRawrbXEq9JY0g0VtzFJHWd7lEYkInKIlkFXIMIHC0vvpEoAhFmrZ5/i1DUOLnZCfg/Z3LiPdeZ2bmKcuzjP6uIpPsneARo7Fil11YXIGpU9QioahPHyV38eTJk55lUVFRZGQoCurq1atrbbNmzRoAYmNjW3JoFRUVFZUgxOmSqLY6KKyykl1i4nBeJQfzKjlVbCK33EJ2iZnD+VUczKvkZFE1eeUWysx2rI7QFo/8ouc6gKMIoG+No8EpyZwoUgeqm0uZyc7RmoH+gV1iiDSEfodSpe0xaDV0r3HAnCiqxulSxdqm8lOIx86dy30X9+LyoUqn2eJwcfM7O8gtb6uZnf6sPtCxYucAusaHEx+hzOrdnVMe0tcgKirgHz3XJTZ4HEUA4Xotk3opg9GFVTb2nCkPbINUVFqAe4JgaowxpCehuPF12B7MUyPHG4XTDJY8sBvB6vAIRd9bkzyrDBdO0VU87bl/RuqCiyb2A2WZ8J9KEWrE9W9GCmR1FplbFH2eDVvGTHEPmhpB6E7H3VTXiBgRgoXubVCvKOac2DmAIzVCkQ4n3Sk6ZwsZxKY7s9oFXYwSi+cj8kQaHRRHKM+rez5oXHKtOkW+jqIo0YxLlrA4Q1AoGjduHABbt271Wz5nzhxkWeZf//oX69at8yz/+OOPefHFFxEEgQkTJrTk0CoqKioqQUKV1cHpEjNH8hVR6GSxifwKKxUWB/Z6Bjddkky1zUlRtY2cUjNHC6o4kFvJ8cJqykyB+UFsCb6OouQO4CgC6JvqvQBV6xQ1nx2+9Ym6qbFzKo2nd02dIrtTIluNAmkyu3xcIiM6gFAkCAJPzx3MyJrnUlhl46a3d1Btc55ny5azxqc+0cUdRCgSBIFh6bEAlJsdnCo2BbZBKiotxO0oSozUE6YPsroNwPT+KZ7bvuKzikooUWV1eH53O8rkwH4+fb5DqlDUOGxFYK8AuwYcTtApAtD3Vq87a5r4E3GiIoBYZT35ctOvnwwnq9EV2wAoiIWlF4rMLo0kztW25/gEoZpxwlEAionlLcdsXLIiHySLRXQSzhVuWkasWO65XS7HUC6HU4girPSkAJ1wrotJUMSYYEQ0KmLROfFz5jhFCDI4Ia0Ysgz+QpGZcNxzliIEEwJgDkVH0axZs5BlmRUrVuByed+4Bx98kPDwcKqrq7n44ovp1KkTUVFRXHvttVitVkRR5MEHH2xx41VUVFRUAovN4SKrxEy5xY6tjngyURCI0GtJjDSQFhdGUpSRaKMOnab2z48ky5jtTgqrrFRZ237gqzVxO4rCdBqijR3DMdLXp0aKWqeo+fhGOKj1iVSaQq8k73fweKHq6msqu7LLAaU+xpC02IC2pbUw6jS89puRZCQoMzsP51dx9we72tRxdqbM7JlhPDgthtSY4Iq0agm+TrNdap0ilRDG7pQ816Jdgqg+kS8X9UuiJlGV1Qfbt86aikpr4Rc33kGEok5RBhIjlRivQ3mVqsO2MVgLlXgxc804uCBQ7NKz1xELQBoFDNYc8ax+WkpHbuLwu2BxEvZzuef+qzNF4tBxUVlkS1vfKOaIuzy335WmcszVw3O/h3iKSFqnbyLiIqpmXxbZgA0jR33qE9WKnXNHuglBKhQB6ONQggK936WIeO/4VmauTLbRjuTzuIQGC8o1djgWdIJIpT0wEwVbJBRNnjyZv/zlL9x4442cPXvWs7xr164sW7aMmJgYZFmmpKQEk8mELMsYDAZef/11xo4d2+LGq6ioqKgElrwKq+diUhQEwmtEofS4cHonRzGgczQ9kyLpHBtGfISBlBgj3RIj6JcaTb/UaLonRpASbSQmTIde6/1JqrQ6QmpGU0FNpyGlg0QQwDmzy/JVoai5bPdxFI1SHUUqTaBXsrcjqApFTaPC4uBooXLeGtA5Oihn1zeX+Ag9b94wipgwpYO87kgRT6w62GbH83UTTevXMdxEbtyOIlDrFKmENrnlFs9M5LS44BRzEyINjKy5DjpZZOJ4oXptqRJ65PkIRakdRCgC6JeqTE4qMzsorLIFuDUhgOk0iAaoNuNWwNfbEpFRbt8kriFSUJzKJjmcYjmx3l3Vh/60GcGlnNjXDBU40E3k6qJotLTPWENXoYQhQhYARcSwShpHvqRE62kEiQGaA63iLIoRKhEF5XlWyLGAN3YOoI+Q67+B5ARBE7yOIgBtlOIscnrPF7p47/uWmSdj1sgU6fydUu46RaIgE691UumwBES4bdG0Z0EQ+Mtf/lLnYzNnzuTYsWMsX76cAwcO4HQ66dWrF7/+9a/p0qVLSw6roqKiohIEVFkdVFoVy6xOI9I7OcpTfL0x6DQiOo3oVxQ7r8JCYZkdWYa/f3mQN2+O8wyGBStVVgcmu/IjnxwdjEUVm0dSlIHYcB3lZgeHQ0i0CyaqbU72n1UiB/okRxEXEaRZyipBia+j6FiBOqDWFPbklHsGTYd3Df3YuXPp2SmSV68bwfWLt+GUZN7Zkk23xAhunNC91Y/lKxRNH5DSwJqhx+D0WEQBJBl2q44ilRDGHTsHwSsUgVLjzO20/vZAAZk+v3MqKqGAv6MoeL9rTaVfajQbjhUDSp2ijhKl3ia4rGA+C7poKCkAvX99Ih1O5ul+8KyeLXWFZog7+hxvJO7nY0WGVxnpb27f92WOsIu9cjcAVkkjmOA6QrhgIVqoQiPI9NKcIFKqJkvKaLJjyk3d9Ym8jqJe5zqKZKlGKAriFBdRr7iKrPlQ4xJyxeqQBAFRlumZp3RSsox2kh3e52GSI+hECQAJWjtZDidWyVFr923e/LbceUJCArfddhv//ve/+e9//8t9992nikQqKioqHQBJlskt97feN0Ukqo/kaCNGnTLzO7/CyiMrfg56+/u+M96Lm4z4iAbWDC0EQfDEzxVW2SipVmeXNZWfssuoqT2qxs6pNJluieGemJ5jqqOoSfyU7XWHDO8A9YnqYlzPBJ68cpDn/hOrDrL2UOvW/agwO9hWM6jbNT7cUzeroxBp0NI7WfmdO5xfiakd6j2pqLQFZ8q88TRpQRo9BzDDR2xefVCtU6QSeuRWeEXZ1A4kprgdRaDWKTov1iJwVIAmEsqrwaDDLgtssCUA8BvxG6Jr3EQVUrRH/GgKYpUDbbkiEBxPgfIYkauKo8+zVevTXzhDD5RzdRZJ/CxncMDVz+MsAkgVCxigOYSO5tXTia0RimQZKuRorLKWLJT9d6GESOGcMQjZqYhEQhALRQD6mvddrnENaUUc0cqk0a5FYLDLteoUmfCOJcWKVuwuJxZn+9cpalOhSEVFRUWlY1JqsmNzKj964Xotsa3k+hEFgdSYMM/g6Ff78nl/2+lW2XdbselEsef2uJ4JAWxJ69M3xXtBekSNn2sy20+VeG6PUoUilSZi0GrolqB0GE4UVSNJwS2aBxO7fISiER1UKAK4amQ6d07pCSiumLuX7ub7w603+Pr9kQJcNZ+7af2TO0y0qi9uIVGS4WefiR8qKqFEqDiK0uPDPdHGe3PK/dwZKiqhQEesUQTnRI6rtWkbxlYEkh1sgNUBBj077XFUycp4yCLtV55Vm+0mOuMV/7f0F7mkNJJ4Z/sLI4IAc8SfPPdXySOQETkp9eC4qweSrDy3aKGKwZp9RNK0z44eG+GC8vtVRSQutJyQU5BqpIpasXOgCC+ikea8ru2KNhI04YoDzU288h6KMnQvgKNG/xqj7ug5gEjRglN2YXa2/2TdVheKZFnmxIkT7Nixgx07dnDixImgnw2uoqKiotJ4nC6JgkrvD17n2Naty6PXin4RXU+sOsiB3OAdvNl8wisGjO9gQpHv7LLDqlDUZHac8g5Wj1brE6k0g8wkxcFhdUicLbecZ20VAJcke+rNpEQb6dyBBnLq4oFpfZgzWInoMNtd3PzOTv67/nir9L/8Yuf6d6z6RG586xTtUusUqYQovo6i9CAWisD/XLLmYH4AW6Ki0nR8axR1jg3u71pT6NkpEr1GGR5WHUXnwXxGqY9jtoLNDkYd31s7AZAmFJIqlgNQIUdRTfOc2Noc7zl9Rx+ByRWBSy0ZLZwgCWUsZp/clayaekuFchL7Xf2xycq4jUFwMFBzkGSh8ROWYn1i5yrcsXN4Y+d6nxs7B4pQpAmBuH9BC4Y4kLxCjzPe2+7MXJlcgw0X3ut1JzrP6xmBGRkZqyuEo+e+/fZbLr30UqKjo+nduzdjx45l7Nix9O7dm+joaC677DJWr17dWodTUVFRUQkQBZVWzwzjuHA94frWn90SptPwq+FKVKndKXH3B7upDsJImCqrwzMDOTMpkqQOFEEA/o6iw/lqp6EpWB0u9uSUA5CREN6hZh2qtB+9fKK+jqmFvxvFkfwqT924ERlxHdIF44soCjx71RBmDVIinWQZnvnmCL/7cA8Wu+s8W9ePzenihyNKkeK4cF2HdWYN86lhpdYpUglV/B1FwRs9BzB9gFcoUuPnVEINt6NIrxWJCw/uOrpNQacRPZOTThZVY3U0//qhQyO5FKFIGwXVVrA7Qaf11CeaKOzzrFohNT1yDkCsdKCrVMSBw12gmxhOhBS4MDBRkJkt7vLcXyUN99yuJoqfXQOpkKM86/bUnKKneBIBqda+zsWvPpEUC8BRubNnWZ2OImSlBlAooHGLycrYmSvO2+7MPBlJlDhr8B/jMsmKKKgVXESLLqqc7e+8bfGnzW63M3/+fGbNmsVXX32FyWRClmW/P5PJxJdffsnMmTOZP38+dnv7Z+z9YjCboawsuP/M5vM/j0YiCILfnyiKxMbGMmnSJN54442gdbN169Yt6AcusrKyEASByZMnB7opjaIl7S0tLeXBBx8kMzMTg8FAUlIS8+bNY8+ePQ1ud+DAAa666io6depEWFgYgwYN4v/+7/+QpPP/KIYqFruLUpNyDhcFoU0Hv2+d1INBXZQLrJPFJh79bH/Qfae3nyr1iGYTOpibCKB3chTuU5XqKGoae3PKsbuUc4HqJlJpLr18Cn0fK1DrFDUGX1dIR61PdC5GnYaX5w/n/mm9Pcu+2JvLVf/bTG4znWibT5R4BLepfZPRajpmYnmPxAhiauJz9+SUBd11hopKY3ALRYmRBk+tz2Clf2q0Jx5vy4kSKiztP1taRaW55NXUKEqNad1EjWDAHT8nyXC0QO331YmzCpwm0IaDSfksnHJFcNKlDO5PE3d4Vq1oRm0iAH2OyXN7Sz+RSRWBF/8vEA4RhfJ8t8q9KZK9/RMHeg66+pEreWvQJYuFDNQcIE4oJRwzInUJj7LHUeSUNVQTgSQLHJOV/cRiIol6JqqKISLSimGKs0hSxCBXtA5Zo5w3MnOV6819Bv/raxPe9ztR46DCbqK9afE08Pnz5/Ppp58iyzJarZZp06YxZswYUlKUNzc/P5/t27ezZs0aHA4HH330EU6nk48//rjFjVc5B7MZliyB0tJAt6Rh4uNhwQIIb70T3sKFCwFwuVycOHGCTZs2sXHjRtauXcvSpUtb7TihRrdu3cjOzlY7vQ2Ql5fHxIkTOXnyJCkpKcycOZP8/HxWrFjBypUrWblyJdOnT6+13ZYtW7jooouwWCyMHj2abt268eOPP3LfffexefNmPvroow538SjLMrkVFo85NinagK4NB470Wg3/mT+M2f/eSLXNyae7zzK+ZwJXjUxvs2M2lU3HfWLnMhMD2JK2IUyvoXtCBCeLTRzJr8IlyWjEjvW5biu2n/L+Fo9W6xOpNBP37E6AY4WqUNQYfin1ic5FEATuuagXfVKiuP+jPZjsLvafreSy/2zk1etGMLKJgvXqAz6xcwM6ZuwcKI6soemx/HC0iOJqOzmlFromBH5QRkWlsdicLgqqlBnHwVyfyI0gCEzvn8Kbm07hlGTWHynk8qFdAt0sFZXzYrI5qbQqA74pHSxFAs6JHM+rYnBabOAaE6w4KsBpBmMKVJlBwBM7BzKjxKMAuGSRapoRFyfLiGcUYUACTvbQcXlp4N0zBsHJdHEvn0hjkRD5WhrKbzQbPI/LiGRJ3aiWI+gpnkIjSEQJJvppjnrWsck6rBixyUYsshEZAZ2gfJ8q5GhkRHJIwIISz9ZbyKP2cJoMCIr4EgpoDYqoJTmU/6KAM06PrthGUgVEm2T2GPTM9hHS3I4igDitnSKHA7u2fR1+LXp1v/zyS1asWIEgCEyZMoU333yTjIyMOtc9ffo0N910E99//z2ffPIJX331FbNmzWrJ4VXOxWZTRKKwMOUvGLFYlDbabK0qFL399tt+99esWcOsWbP48MMPWbBgAXPmzGm1Y6l0LG699VZOnjzJzJkzWbZsGRERyon5s88+Y+7cuSxYsICTJ08SFeUza8LhYMGCBVgsFp5//nnuu+8+AKqrq5k+fTrLli1j1qxZ3HDDDYF4Sm1GpcWBqSb+Ta8VSYxs+2zYjIQInrxyEHcv3Q3AY58fYGh6LL2So86zZfuw+UQxAKIAY7t3PEcRQN/UKE4Wm7A5JbJKTPTs1Lys5V8a27O8QtGYDvrZUGl7enaKRBCUODFVKGocP9U4igxakf4+xZl/KcwYkMKKOyaw6N0d5JRaKK62c+3rW3ni8oFcM7pro/YhSTLfHVKEIoNWZFKvjjcRwpfhXeP44agSs7c7p0wVilRCirxyK+45gaEgFIEiPr+56RQA3x7IV4UilZAg36dGb2oHjJTu53PNdFCtU1Q39gqlRo6ohbJqMOhZVyMU9RFyiBaVz4hb+GgqmgoHhipFFDjUFYZIEQicZ5Km5ACXVYlja8PaPdOEn1nJCOzoWCcP5FfyDqIE/1i0YrkTZlc4fTVHMQo2v8cMggMDDhBqu9Xc7qujsrc+UZ2xc5ITBE3oOIrQgDYCbN5JbK4aoQigZ57MvjQz4H3ffIWiWNGKXXJia+c6RS2aCu4enB8yZAjffPNNvSIRQNeuXfn6668ZOnQoAG+99VZLDq3SEGFhEBkZnH/tJGBNmzaN66+/HlAG/FVU6iInJ4dVq1ah1Wp55ZVXPCIRwBVXXME111xDcXExb775pt92n376KadOnWLIkCEekQggMjKS//znPwA899xz7fMk2glJkv2Kd6bGhCG2k2Pq0iGdubZmcMvicHHXB7tbVHehtSiutnni2AZ2iSGmA+VU+9In2adOUZ4aQ9AYHC6Jn2pcDcnRBtLjQ2PgRiX4CNNrSK+pN3G8oEp1CJ+Hoiob2SVKxPGQtFj02o4Zl3Y++qRE8cWdExlfE4nqcMk8vGIfj32+H4fr/PG4e8+UU1SldGIn9Upsk1qEwcSwrrGe22qdIpVQI6fMG+se7PWJ3IzMiPPUd1l/pEith6ISEuT79IVTYjretb2vUHRIFYrqxl6u/JdlKKuiWhfGNrvi2J7RCrFzmjPemLFtfUXGVtZzTpclxdlkLwWXGTRGcJk8EWdtQbRgZbJwEAAbOr6VhtS5npkI9rgGc9SVSY7UhUIpkSo5Eodc97WkJAuUybEAHPGpT9S7LqFIlkJMKAK0keDjGHLG+9QpypVxGErJ84mbs6HHISsRspGiGbvkxCKFkFC0detWBEHggQceQKc7/xul0+n4/e9/jyzLbN26tSWHVlE5L8OGDQMUMcCNIAh069YNu93O3/72N/r27YvBYOCKK67wrJOTk8Ntt91GRkaGp17NlVdeyY4dO849hF9dnMrKSn73u9+Rnp6O0WikX79+vPDCC+etV/PGG28wePBgwsLCSElJ4bbbbqO8vLzOdZ1OJ6+88grjxo0jOjqasLAwhg4dyv/93//hdHp/FNavX48gCGRnZ3uet/uvW7dujXwFG2bbtm1cddVVpKamotfrSUtLY9GiRZw+fdpvvXvuuQdBEHjllVfq3deIESMQBIGff/7Zb3lOTg533XUXPXv2xGg0Eh8fz5w5c9i8eXOrPIddu5SifN27d69T6J4yZQoAn3/+ud/yL7/8EoB58+bV2mb48OH06NGD/fv3k5WV1SrtDAaKq22eeiuRBi3RxvYdNPrLpf3pU+MiOlJQxd9WHWjX49fFlhPe2LlxHbA+kZu+PjEER/LVTkNjOJBbiblGzBzdPaHDxVCqtC/u+DmT3eUn2KvUxrc+0bCM2MA1JAiIi9Dzzk2juWF8N8+yd7dkc/3ibezJKW9QdPQtMD+9f0q963UUhqTHem77foZUVEIBd30iCB1HkVYjcnE/JdLSbHd5HPoqKsGM/6TJjucoio/QkxytOBsO5VWqk5PqwpqviDJmG1jsbNR1wVEzrD5V3OVZrVlCkSwj1AhFLgFsXcKIkM4ZsnfZFLHKUQ4IENYZovtCdB8wJICzEmi7922WuBsRZUzoK3kYFXLdvzkSGorlRHKkdI5LmexzDWSHayTbnCPZ6xzEEVcvsl3p5EnJHJF6YUP5PrkdRQYcZFDH74LsVGLnQiV6DkATBgi43xdXvNc9lJkHgiCz1uA7liRgrnEVGQQHBsGJNZQcRUVFikW/f//+jd6mb9++ABQXqxcDKm1LVZUy891g8LdfSpLEFVdcwTPPPEPPnj25/PLLSU1VTkj79u1j+PDhvPbaa4SFhXHllVfSq1cvPv30U8aPH8+yZcvqPJbNZmPq1Km8++67jB49mmnTppGdnc3999/PTTfdVG8bH3roIe68805SU1OZOXMmsizz2muvcdlll9X6YbZYLEyfPp077riDo0ePMnbsWKZNm0ZeXh733Xcfc+fO9YhSKSkpLFy40OOQWbhwoeevLnGjqfz3v/9l/PjxrFixgoyMDK644goSEhJYvHgxI0eO5NChQ551FyxYAMAHH3xQ574OHz7Mrl27GDRoEIMHD/Ys37JlC0OGDOHll19Gp9Mxe/ZsBg4cyLfffssFF1zARx991OLnYTIpP8RxcXXXMEhIUE7Ye/fu9Vvuvj98+PA6t3MvP1f4ClUcTonCmpnFAgKdY8PafeDbqNPw8oJhhNUU6F26PYdPd59p1zacy2YfoWhCz44by9M3xSevOl91FDWGHWp9IpVWpJdPnaLjavxcg/jVJ+r6y6lPVB86jcjjlw3g6bmD0NUUz916spQrXt7E5GfX8/zqIxwvrH1eX1MjFAkCTO2X1K5tDgQxYTrP9+xgbqXqblAJKc74OYpCQygCmD7AK0L71kRTUQlW8iu8omxKBxSKwOsqqrQ6yVUnJ/kjucBWBNpwMFnAZmetrMRmanHST1QmTNtlHWaafi7WlNkJr1bG8/ZnCIx0RHqP66xS3EOyHQxxENULYvtDeFfQRinCSXg6aCLA0XYTO5OESqYK+wGwomeFNLpJ27vQYiKCEjmBs3IXTkndKZOVvnKJHEkxyuevp5CPVqhjwr3sqonXC6FJmBqDElXosgMghWuQDIoUk5krgyyz1+AvfJl86lvFaexYJXv7tZcWCkXuQeiSkpLzrOmlrEzpwIW3Yn0aFZVzkWWZVatWAfiJD6C4VI4dO8aRI0f48ssv+fjjj3nllVeQZZkFCxZQXFzMQw89xKFDh1i6dCmbNm1i+fLlSJLETTfdRF5eXq3jbd26FYfDwbFjx/jkk09YuXIl+/bto3Pnzrzzzjv1xt+99957/Pzzz3z77besWLGCAwcOkJmZyYYNG1i3bp3fur///e9Zt24dV199NSdPnmT16tV88cUXHD9+nFmzZvHFF1/w2muvAYog+/bbb5OYqAxev/32256/Z599tkWv7datW7nnnntITU1l+/btbN26lY8//pi9e/fyxhtvUFRUxI033uhZf8yYMWRmZrJp06ZabiOAJUuWAF5BCaCyspK5c+dSWVnJ+++/z+HDh/nkk0/48ccf2bJlC9HR0SxatMgjVjeXTp2UPFm38+pcTp1SsrNLS0uprvYOzrmfR1paWp3buZfXt99QI7/SilQjXCZE6jHWiDXtTWZSFE9cMdBz/5EV+9h/tiIgbQFvfSKdRmBUE4uEhxLpceGE65X3XBWKGse2U771iTruZ0Olfcj0EYrUOkUN85OPUDQ8QxWK3Fw9qisf3jqWTlHeyVPZJWb+/f1xLn7+R2a9uIH//XCC3HILJ4uqPYLkiK5x7VKPMBhwx885JZl9Aby2UFFpKv6OotAZY5nUK9EzAWzNwQJckupeUAluOrqjCM6Jn8tVkyT8cFYpcW+aMDBZkRxO1jkUZ+Qo4RgGQZlkUiFH0xwhw57rvcY/2EtDT6teqT/krADRABHdILofRGaCPgGEc1K9RKMiFiGAy0JbMVfchgFFuPheHkheM2P2zuWIT32i3tQecwWU6DkxxL57okFps1wj9ggCzjglfi7KCsnlUGKsokT29veqZe9veYLGTrU9hISiPn36ADRpZr97Xfe2Kiqticvl4tixY9x0001s2bIFg8Hg3+PUzAABAABJREFUJ1q4efLJJ+nSxb9o5vr169m3bx9du3bl73//u59jYu7cuVxxxRVUV1fXqlfj5tlnn/UIMwA9e/bk0UcfBfDUrTmXJ554wu+7kJiYyG9/+1sAfvzxR8/ywsJCXn/9ddLT03nrrbeIifGejKOioli8eDF6vb7BeLfW4qmnnsLlcvHqq68yYsQIv8duvvlmLrvsMrZt28bu3bs9yxcsWIAsy3W6ij744AMEQWD+/PmeZW+++SZ5eXnce++9fgISwMiRI3n00Ueprq7m/fffb9FzGT16NAaDgYKCAr755hu/x2RZ9tRhA69DDfCIRvUJ3m4R3XebUMVkc1JmVn6YNKJAUlRgB4zmjUjjmlHpAFgdEre99xOlpvb94QRl9qa7DsawrnGE6QMjnrUHoijQqyb273SpGZOt7bKPOwKSJLMjSxGK4sJ1ZHaKPM8WKioN4/7+AXW6P1QU7E6Jn2sG+LslhP9iBI7GMiIjnu8fuJBn5g5mQmYCvsbgg3mVPPn1YcY/9T3XL97uWT6tf3IAWhoYhvs40Har8XMqIUQoRs+BkhZwYW9l0l6Jya7GPqoEPf41ikJssLqRqHWKGsBRUSMUhYPJyj4hkWJJuda8QrPFs1pzY+eMOcrYglOE8ORIRWpyVoEhCWL6gTFFOXZDIpQ+Vomjc1narF5RjGBhTk3MngsNH0njW2W/R33qE/Wpqz4RABJo9PU8FqwIoIvyez/84udyZTRhZ1gnDfAsc0fPAcRpbJhc7evua5FQ5I7Heuutt/wGVOvjvffe480330QQBL+aMCoqLcVdf0er1dK7d2/efvttoqKiWLp0KT179qy17qWXXlprHxs2bADg17/+dZ01t66//nq/9XyJj49n2rRptZZfe+21AGzevLnOWkXTp0+vtax3794Afs6l9evX43A4uOSSSwgLq90BSElJoVevXuzbtw+Lpe1mD0iSxNq1awkPD2fGjBl1rjNp0iQAtm/3DjTUFz+3ZcsWTp48yQUXXEB6erpn+erVqwG48sorG32M5hATE8Mdd9wBKPF8n376KRUVFRw5coRrrrnGL0JPFH95BbFlWSbP12IfbUSrCfzr8NfLBzC0pp7A2XILdy/dhbMRxblbk19K7Jybvj4D1UcL1IHqhjhaWEWFRckRHtUtHlEMIWu8SlDi5ygqUB1F9XEgtwK7U/ktUN1EdRNl1PHrUeksWTSWrY9cxKNz+jMkzX9A42y593ffNxqqozPMTygqD1xDVFSaiDt6rlOUIWCu/+YyfYBXjF59ID+ALVFROT9uR5FWFEiM6JiTUfr71KY9pNam9cdeodTIEbVQbeF7jXf8aoy433NbcRQ1kVIbkSbFVbmvm8AIeyQ4TYp7KTwVaMK5PSwZDIltWq9otrCbGJQyDtvlXhyTW369eKRGKBKQ6SU08HsghlB9IjeaMHzfC2e8V+zKzJMR9aWsFXvglJWxNjNhuGRlDCFGtOJ0tW8kcote4bvvvpuXXnqJ/Px8br75ZpYvX85NN93EmDFjSEpKQhAECgoK2LZtG2+++SZff/01sizTpUsX7rrrrtZ6DioqLFy4EFAG86Ojoxk0aBBXXnllnbVnkpKSatUtAsjNVVTrbt261XkM9/KzZ8/WeiwjI6PObWJiYoiNjaW8vJyysjJPzRs3dUWXRUUpP842m82zLCsrC4DXX3+d119/vc5juSktLa3llqqLN954g40bN/otS0xMbDCarri42OOm0esbVvJ965D16tWLUaNGsWPHDvbt28egQYOAumPnwPt8J0yY0Ohj1MXhw4d56qmnai1/+OGHPfXSnnzySXJycli+fLmfMKXX63nxxRe58847AYiNjfU8FhkZSVlZGWazmbpw1z5yv5ehSqXVidmu/CgZdRriI4Jj9oZBq+HV60Yw56WNFFfb2HS8hH99e4RHZvVrtzZsPu797E3ITGhgzY5BH586RUfyq/wG1FT82a7WJ1JpZSINWjrHGMmtsHKssBpZltu9Tlwo4Bs7N0IVis5LcrSRmyd25+aJ3TlVbOKLPbl8vvcsJ4uUa5iRGXF0T4w4z146Dr2SIokyaKmyOdl1ukz9nqmEBDani4JKpc8YSm4iN1P7JqERBVySzOqDBfxxVj/1e6cStLgnUCZHGzvsRLBuCRHotSJ2p8ThPHVyoB/2cjxuntJKvtcqdamN2EgXlDprVtmAjaa7zSrzK3Ffueb1MNDNKSmxc5HpIDb13K6B8DRwWcFeCfrWiYbzxSg4mCtu401pKgBLXRN4VPMJzT19m2U9p1HGVLpSTLhQV2KMDAi1I/faEBkZp+TE4XLgkGr+XA4kWUJGJtYQR6S+EdfKGiMIGqXelKjBFecdV+uZqwhI1YYqdlp7MlY4BgiYCScKE+GiDVlu30SXFglFERERrFq1iosvvpiysjK+/vprvv7663rXl2WZuLg4Vq1apdYoUmlVGuNoc2M0Ns8m3BYXrY11qbjdSEOHDmXIkCENrluXCFYXGzdu5J133vFblpGR0aBQ5G5HZGQkc+fObXD/AwYM8Lt/3XXXsWPHDj744AOefPJJnE4nH3/8MQaDgXnz5tV5nHnz5nli3OrCLfbUR35+fq3nCHDDDTd4tjUYDCxbtowNGzbwzTffUFRURHp6Otdcc43nPc/MzPR7Xbt27UpZWRlnzpypVQML4MyZM0D9AmKoUG72/kCnRBuDquOWEmPkvwuGM//1rTglmf/9eJKBXWK4dEjn82/cQmRZZlONoyhcr2FwWmybHzPQ9PWZXabWKWqYbapQpNIGZCZHkVthpcLioKjaRlJUx4w8aQm+sUWqUNQ0uidG8LuLe3HPRZkcyK3kWGEVk3p1CnSz2hVRFBiSHsvG48UUVNrIq7DSOTb0Bt5VflnklnvjaEKpPpGb2HA9Y7rHs/lECdklZo4UVNE3pRmz8VVU2hirw0WZWUkM6Kj1iQC0GpE+yVHsO1vBqRITZruTcH0IOjjaAmu+MuAvyxSWONknKKkiv9L+hEZQBvubGzsXl62cy+0aSI6PAmul4goyNHNCqrteUdVxcFpA2/rXM5OFg3zNUPKI5zBd2CV3Z4Rwqln7Oi6nINcEnvUW6qlPJLkUsaWNhCIJCavDitlpxub0TtrXilp0Gh06UUecMY5IQyQRughMdhPHSo8Rrg9DPF9YmyZMabdsB8KQDRpcEVo0Jic9CkDjkhH1hawxD2KseAwAkxxBlKBM3orUtG/0XIu/8cOGDWPfvn387ne/47PPPsNVjyVKo9Hwq1/9ihdeeKFRbgcVlfamc2dlgDk7O7vOx90ul7o+v6dPn65zm8rKSsrLywkLC/NzpDQVt/No4sSJvPTSS83ejy9vv/12kwQ2UBxHRqMRURR56623miQcXH311dx///0sXbqUf/7zn6xevZqioiJ+9atf1XJ+paWlceTIER5++OFadZCawuTJk5HlxtltJ02a5Im0c/Puu+969uPLkCFD2Lt3L7t27WLWrFm19rVrl5LZWpeIFCo4XRKVVmXmglYUiTIG3wXi6O7xPHZpfx77/AAADy3/mZ6dIunfuW07mCeKqimqsnnaoNcGPo6vrfHttB9RhaJ6kWWZHTVCUYReQ/9UdbBDpXXolRTJj0eLADheUK0KRecgy7LHURRl0NIrKbQdvYFCEAQGdolhYJfWn30aCgzrqghFoAiPqlCkEuy4Y+cgNB1FADMGpHginVcfKFCFIpWg5JdQn8hNv1RFKJJlNUnCg+QCWyFow8FiY53FK+DM1uz03C5vRuxcRZmJbjWn8sPdRLrbXSAawNiZFlWL0cVAeGcwZYOkBbF1BRatIHGNuJkXpDkAfChNYKiQ5RHNmsJROdVzu3d99YnkGqGolaLn6hKGjDoj0YZokuKSCNeFY9AaMGqNGLRGjBqD30R/s8NCoamQMksZCWHnEfQErfLZcVTWxNCBK16PxuRE74T0IjhqKOQQYzkjx5MmlGKSvZM/orXtW5O7VUa3OnfuzLJlyzh9+jRLlizhkUce4bbbbuO2227jkUceYcmSJZw+fZqPP/5YFYlUgha3SLBs2bI6Bc/333/fbz1fSkpKWLt2ba3lH374IQDjxo1Do2l+ZvSUKVPQaDSsWrUKh8PR6O3c8XBOZ+tYFbVaLZMnT6aysrLO59sQycnJXHzxxWRnZ7Np06Z6Y+cAT72nTz/9tOWNbiayLPPyyy8DcMstt/g9Nnv2bACWL19ea7vdu3dz8v/Ze+84N+46//85o66VVtre17vrXfcW23FNSOKEBBJIIAmEhJBCTY7ypdxxcMcPuINwcLTjDggtjRKOlsIlgfRqJ7GTuK+7vfb23lVHM78/Ritp7bW3aVU/z8djHyutZqSPtDOamffr8369jh9n2bJlZ7UxTAcGfcGIyOa2m1KqmyiWD22Yx/VrdCHVGwzxid++Ma4Tai7YejSaT7RpfubbzgHk55gpcupddQc7hqYswGYbJ3s9dIVFxDU1+SmR6SXIDMblFHWJnKLTaR3wRuyXVlW7MWSoJYxgblktcooEaUZLfzRTLF2ForcvickpahQ5RYLUpD1GKMr0SQSLYya6HRD2czrKkN6ZY7DBqI/nlGgmzzLpcOT20Aw6ijwd0fN6T5UVSQ2ArUwXFmaLtQQsRaAMMxd5RWul4yxAF3ZayedFbcmMnucQUaFo4RwKRf6Qnx5vD63DrbQPt+MNecm15LKseBmbqzezpWYLF827iCXFS6jJq6HMWUaeLQ+7yXaGG5TdZGNBwQJ8ig9FnUK91eTQM67CnJlTpE8IfFrVJ5uPalFnJZcx2uGUCOJawSgrK+PGG2/krrvu4u677+buu+/mrrvu4sYbb6SsTP/HDwwMsGfPHvbs2RPPlxYIZs3FF1/M8uXLaWpq4qtf/eq4QujDDz/MQw89hMPh4MMf/vCE6//jP/4jvb3RAvKJEyf493//d4BIzs1Mqaio4MMf/jBNTU3ceOONdHZ2nrHM0aNH+ctf/jLub2NdUocOHZrV68fyr//6r8iyzO23384LL7xwxuMjIyPce++9eL3eMx67+eabAfjFL37Bo48+isvl4l3vetcZy33iE5+guLiY//zP/+QXv/hFxIpuDEVRePLJJ9m3b98Z606XU6dO0dXVNe5vXq+Xj3/842zfvp3bbruNdevWjXv8ve99L7W1tezevZsf/vCHkb+Pjo5G/tdf+MIXZj22ZDLgiQqSbnvifGCniyRJfPM9y1gRDuNu7vPy6d/vJKTOnZCx7Vg0n2jT/MI5e51UY1E4p6jfE4x0VAnGE5tPtF7YzgniSEOMUHRUCEVnEJtPtFrMfBXMkFVV7sjtnTFWhgJBqtLcF9tRlH7Wc6AX3ZeHuxj3tQ7ROnDmNaRAkGw6hqLbZWlupncUxQpFQ0kcSQoRGATFAwY7/iEfL0t6na1W7sWl6dd/o5qdINOrmwQ1laoT+iRXvxGqnBKY83RxJy7Iel6R0aHnFcUZSYKbDNHc8z+r6/Fp0xNyQprEUU0X3vIZplA6y3WOFgKDhZnIGIFQgPbRDvq8fbgsLpYVL+OC6gsiwtDiosWUO8vJMedMa4J0lauKEkcJvd7eyReWbegZV3qdSsmLRlvUt2kYLHqN92VtEV7NhAc7YyVplyGNhaKp8Oijj7Jq1SpWr16d6JcWCM6JJEn87ne/o6CggG9961ssXbqUm266iQsuuIBrr70WWZa55557IqJnLBs2bECWZerr67nuuuu4+uqrWbZsGa2trdx8881ce+21sx7fj370I97+9rfzl7/8hfnz53PBBRdw0003cc0119DQ0EBDQwO/+c1vxq1z9dVXA3DppZdy44038tGPfpQvfelLsxrHBRdcwE9+8hPa29u55JJLWL58Oddddx0f+MAH2LBhA4WFhXzkIx/B7z/zy+w973kPdrud3/zmN4yOjnL99ddPmKnkdrsjQtInPvEJampquPLKK/ngBz/IpZdeSlFREe94xzs4evTorN4LwHPPPUdFRQUbN27khhtu4Oqrr6ayspJf/epXXHHFFdx9991nrGMymfjtb3+LzWbj85//PBs2bOCGG26goaGBV199leuvv55bb7111mNLFgFFZdSvz3awGA3YTDPvhksEVpOBn928hoIcfVbGy0d6+N5T8RNHYwmpGq+G7THcdlNWWYstLBE5RZMh8okEc8X4jiKx/53OWydFPpFg9uTlmKkr1Gdw7msdwq9MbKkuEKQKmdBRBHDF0piuov2iq0iQesR2FGVyRhHA4lIhFJ1BcFDvBpGNbG834gnn5Nxq3s6YrjA4A9u5U8NDOMNf403zDNgNZrCV650z8UK26HlFsqyLXXFmgdTB+ZJelxvAwd+086a1/kkK8aPXcc7aTQS6UCRPb98LqkE6Rjvo9fZSmlPCpqrNXDjvQhYXLabMWTZtYeh0jLKRhvwFaGj4lElyhAxW3YIu3H0UcpvQwi9d364hmYZA9uPDzCvaIlQMeNGP605DGlrPzQRhWzOHeL0wMpKaPxN0maQSy5cv56233uJjH/sYIyMj/PnPf+bQoUO85z3vYevWrbz//e+fcD2LxcJzzz3HTTfdxGuvvcaTTz5JVVUV3/ve96adA3Q2bDYbf/vb33jggQdYv349Bw4c4M9//jNvvPEGRUVF/Nu//Rv/+Z//OW6dz3zmM3zlK1/B4XDwl7/8hXvuuSdihzcb7rjjDt544w1uvfVWhoeHeeyxx3jyyScZGRnhgx/8II899hgu15lttw6Hg2uuuSZyfyLbuTE2bNjA3r17+eIXv0hubi4vvvgijzzyCCdPnuSiiy7i/vvv57LLLpv1e1mzZg3XX389HR0d/PWvf+Wll15iyZIl3HPPPfztb3/Dap34YLRp0yZ27NjBddddx9GjR/nrX/9Kfn4+P/jBD/jDH/6QslZtU2HAGz0QpbLtXCzlbhs/vml1xG7o7heO8fieswQhzoL9bYOR7KaNdQXIWWRvtLA0KhSJnKKJ2d6ki4hmoxzpchMI4oHbHrV/FB1FZ/JW2CZMknTrOYFgpoxtP4GQSmObKJAJUpvYjKKKNLbDunxp1Mbpqf1nOmcIBMkmmzKKXHZT5PvkYMcw6hw6daQNgQH0bhB4tjP6XXuhHHXKGpyB7ZzUMhq5LZcawFoGxjnI2TS5wFYBqk8XXOLMDfI2ZHQXoMfUNQxqUz8eHdLKI7cXSOeq36hgMJ/j8SiKqtA52km3p5siexEbqjaysXIjZc5SZCm+Mkipo4QqV/XkXUUGC8hmUMO1NqNMyKULjpXdYPVryGbd6ehpdQWaRiSnSJ5B7tNsSL10csHMsVggPx/6+lJbkMnP18caB6YrOE5l+erqan7xi19Meywul4uf/OQnkVybc9HU1HTWxy6++OKzjtNgMHDLLbdwyy23TGlMRqORb3zjG3zjG9+Y0vKx1NTUnPPzWrVq1YxEsAcffJAHH3xwSsuWlpbyne98h+985zuTLjvZeM/G8uXL+f3vfz/t9QCWLl06YU5RupMutnOns3F+Af965WL+/bFGAP7pz7upL3aMEzhmy1jYLsCm+uyxnYPxNgSio+hMOgZ9NPfpx95VVW4sxtTuxBOkHw3FDrqH/fSMBOgbDZCfM7WLpUzHE1BoDM94XVjiJNeaPsctQeqxujqPh95qBXQBUoR4C1KZsY6iIqcFa4o7AJyLhmIHNQV2mno9bG/qo380QJ44xglSiPEdRekryk6VxWVOWge8jPgVWvq9VBekp7Vl3PB1gMGKpsFz/bogZESlSjsOgKpJ0+4o6pADLDyuizY+ExQUFYC1OL7jjsVSCP4+UEZ04SiOlEsDXCLt41ltBV7MPKyu4zbDi1Na93CMULTwnEIRIJ37HF9RFfp9/QRCQYpziqjLm0+5swyDPHfHR0mSWJDfQOdIB0OBIXLNZ9sOZD2nyNcd+Usoz4xxIIgMzOsCi+UwLb4qWijgIOVUaG0UMQVbuzgjhKJMwm6HD34QJrD8SiksFn2sAoEg5fAGQviC+gmL3WxMu2L37Ztr2Ns6yMM7W/EEQnzs12/wyCc3x62guvVobD5RQVyeM12oL3YgS6BqcKhTzLI+ncOdUfEsNudCIIgXDcWOiFh9tGtE2BuG2d08GMmlWy1s5wSz5LyYjjQ9p6g2aWMRCM6FLxiiK5wZWZXGtnOgF9ouX1rKL146TkjVeO5gF9etqUz2sASCCGMdRQZZinR4ZzKLSnN55oDe3XCgYyi7hSI1BP5uMNo51qtxStE/i8vNJzBr+vXfCDmoTK9u0jQ8xOKw/thZZSDXUalbk80VkhFsZTB8BNQgyPGdWHWdvJ1XQovwY+ZZbRnv0HZRKg2ecx1Ng0OaHu1hI0AVPWdbEpDOOeZ+Xz+eoIcCewH1+fWUO8sxyomRPFxWF3V589nXtReH2YF8NvM2ox2I5q8rbjMW9K6y2k4NU+0hWrgU0LuKNsgH5nroE5I06znBHGG3Q15eav8IkUggSFlibefy0qibaAxJkvjWe5eztFyfyXGqz8Odv32TgKJOsubkBBSVHU16Bk1prjWSY5AtWE0GasLv+XDnCEpo9p9pJnG8O2oHlm3bhiAx1MfkhImcoij7WqMXoecJkVYwSxaWOLGb9WLPzrCloUCQirQNxOYTpf/1dWxO0YuHu8+xpECQeMY6ioqdlojVeSYT6ySR9TlFyhAoXjDYeP5A1Lbt/aY3IrenazsXlDRcJ6Pf4fbK4rh3+UyI2QXmPL2rKM64JQ9XSTsBCGHgD+qmSdfpJpcB9BzWeqkDw9ks1tQQSPJZhbThwDCKqrCmfA0XVl9Itas6YSLRGLXuGtzWPPq9/WdfSLYBcsT+L+SOTmau7dSQLd3kolvK7tDm06omx8FGCEUCgUAgAHRrxjHbOQkJly39hCIAm9nAL29ZS6FDn+31+ok+vvbX/bPOxtt5qh9fUBdHNtUXpEV2U7xZFLbxCygqTb3xD8NMZ473RD2m64ocSRyJIFNpKI5uV0c6RU7RGLGiWTytRgXZidEgs6xcL9a0DnjpHUlxpwZB1jJmOwdQmeYdRQArK91YTXp5alfzQHIHIxDEEFBUesLHgkzPJxpjcVn0fCrrhaLAICgeMNh5tjlaH1kT0+0xqE5P5Nlt9XDeUb024bOAcd78+Ix1UmSwlYBkgFD8z2+ukt/CFe6QeV1r4KhWcs7lD4e7iQAWSm1nX1AL6SLRBB1FATXAoH+QBQULqMurw2RITg3LZrKxoKABn+InqAYnXshg1d+Dqmdeh1wmxipUNZ0aneYAF0v79ccw8JS2Cr+WeBtWIRQJBLNgLBfnhRdeSPZQBIJZM+oPEQx3iTitRoyG9D1ElLtt/OKWNZiN+nv4/fZTPLCtaVbPuTU2n2h+duUTjbGwJDq77JDIKRrHiXFCkegoEsSfWKHoaJcQisaI/SzmC5FWEAdWVEYLPntiOtYE6UHviB9PQEn2MOac8UJR+ncUGQ0yyyv0fe9Un0eItIKUIdZeuioD9rWpMK8gB1s49+xAe5Zf8wUHgRDDipE3hvRrvBrDCA6lGYCQJjHM9M4/h3tGsYWNXIaqc8GYQAHS6ARLAYRGgdlNpD0dmxTkWnl75P5/hN7Lr0MX0nGWjqtDMflEC5hMKJLhtC4hFZWu0S6qXdU0FDTMbvBxoDK3kjJnKT3es+QKyWZdLFLD/3yjjJqrC1tV3dAvKVwk70UK29M9py5nREt8XSF9q4ACgUAgiCsDnqjtnDsNbedOZ3V1Ht+5bnnk/r8/1shLs7CyePVY1DN3c3125RONETtb/1BHls8uO43j3bpQlGs1UiACmAVzQIHDErEEFUKRjqZpHAvve+UuKzkWEb8qmD0rYiwM9zQLoShd0DSNX750nPXfepaLv/sCzX2Z3fnc0h99f5nQUQTjMx53twwkbRwCQSyvHY8WfdfWZEcWokGWItd9p/o8DPvO0iGRDQQGANh7UkEJl9Dfaz6IpOli/aDmRJtGaV1Fo7AlKoRbaxOdhSiBtQRkCyi+uD/7JdJ+KtD3GS9m/q6dxxdCt/Dd0LvZrVajxmhTYx1FMir1UufZn1QL6QLLaZ9zr6cXt8XNsuJlCbeamwijbKQhvwEZCY/inXghkxO06P6kuPVrO6MKFb2gWfpZLTUB0IeDI1ri8/qEUCQQCAQCVFVjMHwCKEsSudb0F4oA3nteJXderLdyqxp88sG3ONY9/QLrqF+JZBXUFeZQ5sqMC/LpEmtDcFB0FEXwBkK0hrMCaoscWWlLKEgMDcX6Ptgx5GMomy/aw/SMBBj06p/D/GLRTSSIDysqojNf97YOJG8ggikTUjW+/tf93PXEARRVo2vYz+f+sCuj8xSbM8x6DmBVVbQIv0tkhAlShO0n+iK319XmJ3EkiSU2pyirnSR8HSBbaWyOnndfaNgXuT00zXyiVrNCXZuuloRkCJUkYZsy2MFaDCEP8e4qMkoq/2J4mEukfZjQu3s1JHZqtXxHfQ//FLqZp9QV9GoOmtFdWubRjVU6x3WNFtKFrRhGAiOomsqykmU4zKlzDVCcU0y1q5o+by/aRJ+tYex4Hd4GYnKKajo12s0Kl0l7In97Wl01h6OdmClLbr/+9a/j8oJbt26Ny/MIBAKBIH4M+4KEwtM7XDYTcgaFdP7T5Qs52jXC042dDPsUPvrAGzz8D5tw26fe9bGjqQ8l/PlsnJ+d3USg2y3YzQY8gZAQimKItZ2bXyhs5wRzR32Jg+1NesHiaNcIq6uzY2br2YjtrKoXQpEgTswrsJNrNTLkU9jdMoimaWICQArjDYT4zP/u5OnG8bOR3zjZz0+eP8b/uyz5djRzQWxHUbk7M4SilVXRgutOkVMkSAFUVWNH+LzLZTOxoDh7shCXnJZTtLYme0SyCGoIfN1gtNPYFT0PmK8djdwenKZQdFweYUW4SW2gyIpsNMRlqNPGUgT+HlBGwRjfc+g8ycPHDM/xAW0bL2hLeFpdQQ+68NhOPverF/MbLowsv0Bqn+QZNTBEhaKgGmTAP8Cy4mWUO8vPsV7ikSSJhvwGOkY6GPIP4bKctn3IVj1vSVVANo0Timo7NDoqFd4pnaKEATpx81hoPe9SngISV3uZslB02223iRPkFGC2YewCgUAwEQPe6AyOZNvOxft7TpYlfnjDKq6/exsHO4Y50TPKJx98i/tvX4dpijlM22LyiTbXZ2c+EeifZUOJk93NA5zq8zDqV4TVE3C8J1qsFvlEgrlkXE5RpxCKjnYLoUgQfyRJYkWlm1eO9tA97KdzyJ81AebpRs+In4888Aa7w6KCUZb46IV1/OKlY6ga/PdzR7igoZA18zLvu3Iso6jYacFqSlKhMc5UuG0UOiz0jPjZ3TyAqmoZNXlNkH4c7R6h36NfJ59fk59V2+OimI6ixmzNKVKG9K4byUHjkF7QN6OQq7YA4NdkRpnOtZ+GdyDaDaoVJfHYJJvBVgojJ8IZQPE/jjglH++W3uJKaSdvanU8pa2kMWylFiL6egulc+QTAaDp4gqgodE12kWls5KG/NScCJJrzWV+Xj17unbjNDuRpZiak8EKsgnUYFgoitbeajo19poVZAkuk/fyO/VCOsnnv4LXA/+RsPFPy3pO07S4/Aimj9Go7xR+vwh1FAgE8UUJqQz59LZgo0HGkeTCfzCon4wbDPE7WXFYjPzq1rWR7JitR3v5xmONU15/69FoPtGGuuztKAJYVBKdXRYb7prNnOiOdhTVFYlitWDuaIiZyXqkS+x/x2I7isS+J4gjKyqjM0BFVkpqcrx7hGt/ui0iEjksRu6/fR1feuciPr1FLx6FVI3P/mFnxuVr+IIhuof1ukCm2M6BLtKO5RQN+RRO9I6eewWBYI55PcZ2bkNddnXULCqNtRzP0mzawCAoHvw9QY6GdOHsCuMBJE3Pdu4LOYCpi4chZRR7byhy31KY5LqCuQCMuaDMbfapQdJYJx/jK4aH+Lbhd+Ns6SwEWSK1nmNtDZB0cQXo9fbitOSyrHgZJkPqxiXU5M0j31ZAv69//AOSAYw5kZwizWwgZNfrXjVd0GnQt62LpMbIZ/SqtCRxA2caHUX33XffXI5DMAlGo5GcnBz6+vpwOp1xLaAKBILsZtAXjIj4bpspqd2jmqYxODiIxWLBZIrvgb8yz87PPrSGm375GsGQxq9fPUlDiZMPbZh3zvX6RwM0tusnx0vKcsnPmbplXSayMOai4VDHMOdleUcDwPGeWKFIdBQJ5o6GkqgYcqRrbi/q0gFhPSeYK1ZUuiO397QMcMXS0uQNRnAGb57s46MPvBGZ6V+aa+W+28+PZGp8eks9rxzt4c2T/TT3efnao/v5wQ2rkjji+NIaMyO9Kt+exJHEn/Oq3TxzQLcR3N08wHwxCUCQRLI1nwjAaTVRlW+juc/LoY7h7OzwCw6CFuLIKQUl3BVyhXF35OEBLXfqOpEa4qTFx/yY5plQsTt+Y50JkhFsJTB8NGyFNvcThqul3ogt3U6thkqpl1zJe/YV1BBIMkgmRoOjKKrCeaWrybXmnn2dFMBqtFLtqmJXx64zHzQ6dNu/MCG3GYPHizUIjIZQ0XBIfjZLh3hBW4pPspz5HHPIlLeCW2+9dS7HIZgChYWFNDc3c+LECVwuFzabDYPBICwBBQLBrOgdHEVT9JktNtmIz+dL+Bg0TSMYDDI4OMjIyAgVFRVz8jrn1+Rz13uX88U/6wGBX//rfirdNi5eWHTW79LXjvcy1gy7uT67u4ng9NlloqMB9FnNAJIENQVCKBLMHcVOC06rkWGfwpFOIRSNCUV5dhMFjsReRAkym9iOoj0tg0kcieB0/ra3nc/+YRd+RQX085L7bj+fMle0s8ZokPmvG1Zx5Y9eZtiv8NDOVi5aWMQ1q+bm/DLRjNnOQWZ1FAGRjiKAXc0DXLu6MnmDEWQ1mqbx+nHdfjzHbGBJWWoXpueCxaW5NPd58QRCnOzzUJttWayBAZAkGpujXamrpIN6kwswouVNXShShjiUZ+baNj1fzuMwouWkgK2tOU//CQyA2Z2wl3VKPt4mHZx8QdUPsgVFMtLv7WZx4WIqc9PjWO40O5GQUFGROc1+DomxbqmQ2wxt+nG9slulzxaiUDFymbyHF0JLEz5uESyQRtjtdmpra+nq6qK/v5+enp7JVxIIBIJzoKgaHYO6MGQySJi9yT1ZsVgsVFRUkJs7dyfi719bxZHOYX758glCqsbt9++gwm1jy6JiLllUxKb5heO83rcei37XbpqfvflEY5zeUZTtaJrG8bD1XLnLljE5AYLURJIkGoodvHVqgNYBL56Agt2cnafzw74gHUP68Ut0EwniTZnLGslK2ds6iKZpYnJeCnDPKyf45uON4ybw3H3zGnKtZ3ahV+Xb+cZ7lvHZP+wC4CsP72N1dV5GdOC09Hsityvz0v/9xLKi0oUkgabpQpFAkCxO9nroCls8rqnJxzjFbNtMYnFZLk816h1+B9qHsk8o8nXAqETj4Fg+UZAy7RQAHtVIUJ7i92/IC7KRAZ+id40ASrK7iSLIYC2F4BCoAT27KJVQA2jWEjo9PZQ7y1lYuDBtzsdyzA4sRgsBJYDVGFNnM1j17i01CLJZF4rC1HZqtC9QKFSM1EndzKeDIyRWpM7OK8s0xmw2U1lZGZl9r6pqsockEAjSmAe3n+Sel7sAuH1TDRtW1iRtLAaDIe52c2fjS+9czNGuEZ4/1A3oFh6/ee0kv3ntJBajzKb5BWHhqJhtx/SZZEZZ4vwssxyYiAKHhSKnhe5hPwc7hrK+eNYzEmDYr/sHC9s5QSKoDwtFAMe6Rlke0/mQTRyLyQYTQpEg3kiSxIpKF88d7GLAE6S5z0t1QWYV5NONX718nG8+fiBy/9rVFXz72hWYjWcv3r7nvAqeP9TFo7vaGPYrfO4Pu/jfj29I+4JvJncUOa0m6oscHOka4UD7EL5gSEzCESSFWNu59Vl6Dbg4povqQPsQVy4vS+JoEowaAl839AdoVFxggNXyEQzh3JguxQ5TseLTVAh5CeZUYO86GvmzoSif0DlWSygmJ1jywdcF5lTa1jVAo09RcVgdLC9ejtmQYkLWOXCYcrAabfgU33ihSLboP2FhTsmL1sBqOuDQMoXl4fkgb5f3cIQLEjpuIRSlKZIkYTanzw4iEAhSkz+82UnrsH6KcvmKaqzWFGh/TgAGWeLnH1rL/+44xVP7O3n9RC/BkD491a+oPH+oWxeRHt0fWWdllRuHRRw2Qbd56R720+8J0j3spzg3O7abiRiznQOEj74gITQUR7v6jnQNZ61QFJtPJPY9wVwwJhQB7G4ZEEJRElFVjZ+9eCxy/zOXNvC5yxqmNFHlG+9Zxpsn+2np9/LGyX5++sIxPnNpw1wOd84ZLxRl3na5qsrNka4RgiGN/W1DrJkn8jBTGU3TeGJvB4/uaiXHYmRRqZOFpU4Wl+VS7LSk7YSy14VQNM5u70A4szdrCA5CyIPa4aVR1v//7zDsijzcqzpgKnMOgsNgzOW408b8PdGJ/qGiVDp/l8BSCoFBvfvJkCITEEIBPCoEJCMri5fisqbSZzY5siyTb8vj1OCp0x6RwOjUO9YAzWogaJEx+VVqOzVeMEWtDjdIR/iLtoTmBI5bVLwEAoEgSznQPsShTt06bM28vKwrgJiNMrdsrOGWjTWM+BW2Hu3h+YNdPH+oi84h/xnLb54v8onGWFji5OUjuiXfwY7h7BaKeqJdDVlnxyBICvUlUVHkSFf25hSNE4pER5FgDhifUzTAu1eWJ3E02c2e1kF6RgIAXLa4mM+/fcGU1821mvivG1bx/p+/iqrBj549wub6wrQWH2Kt58rdmXcOtqrazZ/ebAF0+7l0/l9lOke7hvnqo/sjDgynk2c3sag0l0VlThaVOiO3LcbU7xLb3qS/J4tRztpJOZV5NhwWIyN+hQPtWWY5HhwE7zDNpwKMSPok/QsM+yIPD2luJk3HDPn1KBp7BQctfbyzVZ+YGjJKhAqc51430RjtqJYi1NFTGCMZOsklqHjoC2ksLlpKVW5VsoczI9xWN8f7j5/5gNFOJOxKkgi5TZg6/eR6wRsIRBYzSyG+HvoN70rMcPWhJfC1BAKBQJBCPLKzNXL7PeelRyDgXOGwGLliaSlXLC1F0/TZi88f7OK5Q13sah4g12ri/een58nJXHB6TtHbFhQlcTTJJbajSFjPCRJBQ4wocqRTCEUA9aKjSDAHrKh0R27vaRlM3kAEkc4ugEsXl0x7/bU1+Xx6SwM/evYIIVXjs3/YyROfuRDnBNlG6UBzn95RVJJrSYuC+3RZGbPviZyi1GTEr/Dfzx7h3ldOoKjaWZfr9wR59Xgvrx6PCkkluRZ++P5VbKpP3ezXtgFvZD87r9qdkfvZVJBliYWlTt482U/rgJdBTxCXPT2/N6dNcBB6h2gcsYMRcvBSh94ZMqyaQZ6s60aD0AhYK8CUS2vwCCUD+iOBQifIybFADWkhgqEgATVIMBQgqAYJqWETPC2EHFJRh9pw2QrIMSavI1DVNDp9Q1QWrmBR4aL4jENRIBgEW+I6phxmBzIyKipybAuawQrIusWhbAC3BTr1ycrWPgXNoCGFxbpE/weEUCQQCARZiKpqPLqrDdCzd67KJr/hSZAkiWUVLpZVuPj0pQ2M+BWsRjnt/ezjyaLSqA3BwY4sm112GsdjclLqRLFakADKXTbsZgOeQIijXdm7/x0Li7Q2k4EKd4pYZAgyikKHhQq3jdYBL/taBwmpGoap5BEI4s7zMULRJQuLZ/Qcn95Sz8tHunnr1ADNfV6+9uh+fnDDqjiNMHH4giF6RvRiUibazoFucWw1yfiCKrua+5M9HEEMmqbxf3vauevxxnEODJV5Nr76riVU5tk52DHEwY5h3b2iY5iu4fFODZ1Dfm6+53W+cPlC7rxoPnIKfq/G5hOtq81uV4nFZbpQBHCwY4j1dVnyeQQGoWeURrUGgCXSSWR067guxYZJnqScHhwGgwNspfikENbO6Dm7VJTYLkkVlV5PL4FQAFmSMRlMmA1mnBYnTrMTh9mB1WjVc3S87bR1vExbwEdr0IPdYMFlsmFIsLDV7e0nz5zDirI1mAxxECcVBQ4dAosFVBXKy8Ex99fuDrMTi9GCT/FjN8ZcrxhsIJtBCwA2FHf0PZZ3awxXqOSGkiNQC6FIIBAIspDXTvTSMeQD4OKFReTniMyzsyFyic6kocSBLIGqwaHOLPOrPo0TYes5q0mmLIst+ASJQ5Yl6osd7GkZ5FSfJyuDvv1KiJO9+r5XV5STkkUmQWawvMJF64CX0UCIEz0j1BenmFVMFtA15GNvq97RtbQ8l1LXzI61RoPMjz5wHu/80cuM+BUe2tnK5UtLecey0ngOd86JzSeqystMkdxokFle4WJHUz/NfV56R/wUOCY1eRLMMYc7h/nao/vHdQeZjTJ3XjSfOy+eHzkXWVKeO2693hE/hzqGOdAxzFP7O3j9RB+qBt998hBvneznB+9flXJdKiKfKMri03KKskYo8nVAywiNJr3zbaEcTYkZCFmQTec491SDoKlgLwfZzFFTLw1t0Xwipdg9V6M+g4AaoGu0izxrHqtKV5FjzomIQsaJxK6cIkpkhYaBg7RpVk6O9tLhG8AgGXCb7VjjIdpMwmDAg0FTWFFQhyMnDra/mgbHjkFdHWzaBPv26aJRa6suGDnn7tzObrJhM9nwK77xQpFkBKMtLCjaCLmj9bjaTo32WoVcb3Ku78T0aIFAIMhCntjbHrl9zarstp0TTB+ryUBNOI/ncOcISkidZI3MJBhSOdWn5wTUFjpEsVqQMOrD9nOqNr6rLVto6vEw5nRTL/KJBHPIiqpoLsXuZmE/lwxeONQdub1l0cy6icaoyrfzjfcsjdz/4dOHUc9hm5WKxOYTZWpHEcCqKnfktrCfSy7DviDffKyRK3/08jiR6LLFxTzzuYv43NsXnHPCSoHDwqb6Qj5yQS0PfmwDn72sgTEXqWcPdnHV/7zM3hSz99x+Qn+fRllidXV2Z2SNF4qypJNdVaD7FPR4aZR1YWyF3BR5eFibZMKCMgKWQjDr285BywALWqPHGqUoMZlXw4Fhuka7qHZVs7FqI/Pc8yi0F+IwOyYWiQAkGXIX4rAVssBq5aLSxWwoaqDElstg0EOrp4/hoBdNm5tjp08JMqL4WOJwUZK3ACbr3JoKJ05AYSFceSUsWgTXXQcf/CCsWAHd3XDwIAzNzeRXWZLJt+XjU3xnPmh0gqbb/qkOI8GwBlfTqdFhVuZkPFNBCEUCgUCQhYydjEsSXDLLi25BdrIonFMUUFSaej2TLJ2ZnOrzRHzZ6wpFPpEgcTTEdDUc7c6+nCKRTyRIFCsq3JHbe1oGkjaObObZg52R2/E4Z33PqgrOq3YDcKhzmKcPdJ57hRQjtqOoMkM7igBWVUWL87uFUJQ0giGVG3/5Gr+KySKqzrdzz61r+dWt51NdMD2x0iBLfPayBdx/+zrywl1ELf1errt7Gw++fmrOis/ToWfEz7HwJJwVlS5s5uzq2j6dRaXOiLB3oCNLnCSCQ9DeQZ/HSLumb+Mr5ZORh33auc49w9uw2c1Yyf2o3EddeJ5uMNeGZptbNxcVla7RLryKl+XFy1lbvhaHeRrnyyYn5C4E1YcZlaqcAjYWNXBh8SIW5JaiaCpt3n4CY9lGcUJRVXr8Q8x3FFNnzwNLHLrX2trAbIZ3vhNKwx3EkgQ1NfDe98KHPgTnnQd9fbpgNBh/0dptdaNqE0ysNdjQtxcNJAlvnv6dWDQEfWog7uOYKkIoEggEgixDCamRXJmaghxhrSaYEQtLorPLDmVpTtH4fCIhFAkSR2wXzdHO7Nv/xglFoqNIMIcsr4zO+t3Tmloz3rMBvxLilSM9AOTnmFlZ6Z71c0qSxKe31Efu//i5oylRnJ4q44WiDO4oCot5ADuFUJQ0Ht3Vxr5WXRywGGU+d9kCnvrc27h0ccmsnveiBUU8/pkLI51jgZDKvzy8ly/8cTfeQHyLz9Nlh8gnGofdbKSmQL/OOdQxnB1OEsFBaO2i0Ti2nWvUSK0AeFQTsnwOoScU0LNnjPpnNioFMfWOYA5v1mqRe+7GjW411zbcht2cw/qK9SwuWnz27qFzYa8GeyX49a5eWZIptDpZmV/DBcULqbQX0OUbZCjgneSJpoamaXT6Bii1uVnizEc25kQ6smZMXx94PPD2t8P8+Wc+LklQXQ3XXAM33wxr18LAgC4YKfHr6Mkx5yBJEiHttO82g1W3oFPDr+WKblfyQJoKRR0dHfEah0AgEAgSRFPvKH5FP8FbXCa89gUzY2FpdNs5lC2zy07jRE+0WC2EIkEiaYgRR450ZWFHUbcQigSJwWUzURvuGG1sGyKYDQWyFGLHiX5Gw0XjixcUYYiTxeslC4tZErZT2ts6yAuHuydZI3UYbz2XuR1F5S4rRU49l2h380DaWQRmAiFV46cvHI3cv//2dfy/yxrilotY7rbxx09s5LZNNZG/PbSzlff8ZCvHk9gtLfKJzmSsZuBXVJp6s8Dy2NMDp3potOv5OJVSDxb8AAypFkznEl5Uvy4Syfr31yHzAA1tMbZzxXNnOzcSGKFrtIsqVzUbKzdQ5iyb+ZPJBshdBAa7LpzF4DLbOb+wjpV51QRUhXbvACF1dudHvf4RnCYbK/KrsWoBsOSBcRaTIUZGoLMTLroIVq4897KSBFVV8O536x1G8+bpdnVxwmlyYjVa8Sv+8Q8YLPp2Eu4ekmOEopy+NLWeq66u5pprruHRRx8lFEqu6i8QCASCqbG/LVrUX1KWe44lBYKzsyhGKDooOoqoKxTFakHiqMq3Yzbqp/FZKRSF37NBlphXIERawdyyvEIv6vgVlcNZ2MGXTJ472BW5HU+r5NO7iv7n2SNp01U01lEkSVDmniQnI42RJCnSbTLkUziRDcXpFONv+9oj57rra/PZOD/+3TVmo8zXr17Kf994HvawxduhzmGu/vFWXj3WO8nac8OYUCRJsKYmu/OJxlhcGq0ZNGZDTlHrCRjy0WgsAmChdCryUH/IjEk+h1iqKWCKcd4w94/LJwrNgVCkotLt6cajeFhWvJzzy9fitMRhQrDZDc4FEByOdr2EMcoGFrrK2VjcQIElh3ZvPx5lZl0ww0EfIU1lmbsStzlHF06ss+ha9Pvh5ElYvx42bybinTgVKirg0kvBaoWursmXnwI2kw270Y4vdHpOkQymHFCDAKhuS+SR0m4Vr5ycyUmzEooUReGxxx7j2muvpaKign/6p3+isbExXmMTCAQCwRwQG0K5WAhFghlSnW/HZope0GUjsUJRregoEiQQgywxP5zN09QzSkDJni6HkKpFZhrPK4gKZgLBXLEi1n4uxQLXM53nD+lFGoMs8bYFRXF97iuWlka6M986NcCrx5NTlJ4uY0JRidOKxZjZ2SljQhHArlMDSRtHNqKqGj9+LtpN9OktDXP6elevLOevn9oc6RIe8St8/NdvcKA9sa4Fg54gB8NOCUvKcsm1mhL6+qnKopiaQaL/J0nh5CEIQqOqH/+XxghFgyEz8tmEB1UByRCxnQNdKFrYogtFqslAyB3/yYWdI53YjDbWVaxjyUyt5s6GoxZs5eCfWDQpsuayoaiBha4yBoMeenzDqNOYeOEPKQwGPSx0lVGZU6CLRLIpnPE0AxQFjh6FZct0wccwg+PkvHlw4YXQ2wve2VvrSZJEgb0An3K6UAQYcgD9Oi6UayIUvqyp7dToMCWnq2hWV1af+9znKCoqQtM0urq6+MEPfsDy5cvZsGEDv/zlLxkezs7CkUAgEKQyjTEnd0vKhVAkmBmyLLEg3FV0stfDqD957dHJ4njYeq7QYREXkoKEM1bgVFSNk1k007q13xuxT60vEp18grlnRUwuzp6WgaSNI9s40TPKiR79u23tvDxctvgeZ2VZ4lOnZRWlOt5AiJ4R3bomk23nxhgnFImcooTy7MGuiGPAqio3m+vnPqunvtjJo5/czMULdVF42K9w233baR2ITwbKVHjjZB9jNe71Ip8oQmzNYF+m5/WputDgszs5puiCzxpD1IZsQLWcbU3ddk62RizTBmU/AY+H/HDzf6jIBXGyUB3DE/RgMphYU76Gcmd5XJ8bANkIuYt1i7TgxDV+q8HMirx5rCucj9lgpM3bT0A9e20gpKoEVAWvEqDbN0SNo5AFuWGbPGUUjA4wzaDzStPg2DGoq4N3vlPvCpopa9fqlnUnTsAsbfUAci25E3cuG6yABGggSwzl6cJWeR90S8FZv+5MmJVQ9P3vf5/W1lYeeeQRrr76agwGA5qmsWPHDu644w7Kysq49dZbeeGFF+I0XIFAIBDMlrFZQG67idLczLWsEMw9i0qiLe3ZZscz6A3SM6K314t8IkEyyNacoqPd0e8akU8kSATLKnIjdR3RUZQ4Ym3ntsTRdi6Wq5aXUVOgF/S2HevlzZN9k6yRXFoHsiOfaIwVla6IY5AQihKHpmn8+Lkjkfuf3lKPNB3rplmQYzHy0w+uZmVYJOwc8nPrvdsZ8CQm2H17TD7ROpFPFKHcZaXQoeen7GkZTBurzhnR3QydPRzKnUcoXDJfKDcDoGoSPu0cuTlqICxwhF03zAMsbJ3bfKJ+3wAVzgoK7HMobFoLwFmvZxWpE8fOSJJEVU4Bm4oXMC+nkG7fEO3eAVo9fbR6+sO/9Z8u/xD9gVE8ip8Sm4tl7iqMY3Z+IQ9YS3WBarqcOAGFhXDlleCa5WdtMsEll0BZGZw6Nfnyk+AwO5AlGeV0Ac1g0d9r2H7On6dPipE1CAz7T3+ahDBrrwaDwcDVV1/NI488QktLC9/97ndZsmQJmqbh8Xj47W9/y6WXXkp9fT133XUXLS0t8Ri3QCAQCGZA97Cf7vABZ0lZbsJO+gWZycKYnKJDWZZTNDbLGWC+EIoESaChJEYo6swioShGFBNCkSAR2M1GGor1492hjmF8QZHNmwieO9gZuT1XQpHRIPMPl8RkFaV4V1Fzf7Szoip/FiHfaYLTaop0jh5oHxL7XoJ45WgPu8Oi+OKy3Dnb/86G3Wzk3lvXUluon18f7Rrhow+8kZD//2sxQtH5Ip8ogiRJrAx31w56g5zs9Zx7hXSm+QgMjdBo1rtzzAQpRp+4MKpZMZ1VwND0H1P03PRgjO0cgFIUX6HIo3gxG03U5NXE9XknxDEfrMXg7znnYrkmG2sKalmdX0uto4ilrkpW5lWztqCODUUNXFCykItKFrOldCmXlC1lc/EC7MZwl5amAhJYZiB6tbWB2QzveAeUlk5//YnIz4ctWyAUgoGBWT2Vw+zAarTiD50m/shmkMwRoUjOjXasmQbSsKPodIqLi/nCF77A3r17ef311/nEJz6By+VC0zSOHz/OV7/6VWpra3nHO97Bn/70J4LB5LxpgUAgyFZiPYVFPpFgtiyKEYoOZplQNJaRAlBXKIrVgsRTXxzd/452C6FIIJhLlodzihRVy458hiQz4lciM/sr82xzuq+/97wKKtx6d84Lh7rZm8JdYy0xQlE2dBRB1H5OUTX2t6Xu/yaTiBVMP3VJ4rqJYilwWHjg9nUUOvSi6Rsn+/nM73cSUueuk2XUr0Rs1RqKHRQ4zmExloXE2rDuzmQb1qYjIEFjSD/u10utyOjb3aBqwWw4i1AUCuj2bMaoiH/IPEBDTEdRKM5CUb+3n3JnBfnWBHS/Gcy6BZ0k6fZw58AoG5ifW8LqglqW5FWywFVGrbOYqpwCymx5FFqduMx2HCZrtJMIIOQFg236+UTDwzAyApddBvX1ky8/HRYuhA0boLUVAjPvbLQareSYcibIKZLBaNMtDwFLjFDk7MsAoSiW888/n7vvvpv29nZ++9vfctlllyFJEqFQiKeffpoPfOADlJWV8dnPfpYDBw7M1TAEAoFAEMO4fCIhFAlmSTZ3FB3vjp4gj814FAgSybwCOyaDXrw5kkXWj7FC0XyRUSRIECsro8UdYT8397xypJtgSC+ubVlUPKeFapNB5o6L50fu//j5I+dYOrm09Mdaz2V+RxHAqmp35PauZrHvzTWvH++NiLTzi3J4x7I4zcyfAdUFdu6//XxyzHoh+anGTr7+1/1zZnv21qn+iBAlbOfOZGVV9DiYsVaQwSAcPQxOK41BvVaySIrajg2EzJhihY1YVL8uEsm6tX+Pwcuw6qU23BwbcuegWeKXtedVvJgMRmrdNYkTc20lemdRoD/c/RNnlBEw540T2yZfR4GTJ6OZQvFGkuCCC2DRIt3absZPI1FgLzizowjAmAPoHZOq28zYJ1varRKUEm/zOGdC0RgWi4VNmzaxceNGCgsLkSQJTdPQNI2+vj7+53/+h2XLlnHttddyYhYfukAgEAgmR3QUCeJJgcNCkVOf9XKwYyiz/apP43hPTEeRsJ4TJAGTQaamQN/2jveMooTm4IItxdA0jWNhkbbcZSXHMgP/coFgBizPlpnUKUIi8olied+aSorD5zNP7u/kYEdqdo1lc0cRZHBxOoX48fPRbqJPXlKPQU6uTfmyChd337wGY3gcv3ntJD994dicvFZsPtH6ujnMe0lTVsYeBzN1X+zogO42VJeTA4o+IXKtIVqn7lNMmKSzCEWaEs4n0jloHqCuAwzhy+N45xPp3UTlFNgSvK0668FSCL6O+ItFahCsJdNbp6kJ6urgootAniOJw2qFSy+F3Fzd4m6G5FpyUSf6zOSx3HANjDL9bv19VHdDlyHxXUVzJhR5vV5+85vfsGXLFurr6/nmN79JV1cXmqaxZMkSvvWtb3HTTTdhtVrRNI1HH32UtWvXiu4igSCDONQxzJcf2sv6bz3Dlx/amxVFrFSnsU2/8DUZJGHZI4gLY/Zz/Z5gJP8qGxjrKDLKUlbkBAhSk7GcooCijsuuyFR6RgIMevULpvniGCZIIIvLnJEOvlS2JssEVFXj+UPdANhMBjYkoGBrNRn4xEXRrqKfPD83hejZ0tKndxRJEpS5skMoWljixGbSC7O7mvuTPJrMZnfzAC8f0fNHqvJtXL2yPMkj0nnbgiL+8/oVkfvfffIQf3qjOe6v83qMULSuRnQUnU5ejpl5Bfo1z/62IYKZWNtpbQbvEE2mYjyaPhlppaEp8vCgapm4e0dVQDKEO0N0Dpn7WRBjO6cUu+M2TJ/iwyAbqHXXJt4a0miD/PN0e7h4ikVqAGST3lE0Vbq79VyiLVvAMcfXBWVlcPHFUZu7GeAwOzDKRpSwzVwE2QySEVS9q2i4QD/mmUIw4kl8fSXuQtG2bdv42Mc+RmlpKbfddhsvvvgiqqpit9u5/fbb2bp1K/v27eNLX/oSv/3tb2lra+PrX/86ZrOZgYEBvvKVr8R7SAKBIIGEVI2n9ndw0y9f44r/eonfbz9F55Cf328/xb88vDerOg5SDV8wxPEevbhdX+zEbJzzplJBFrCwJPtyilRVo6lX35eqC+yYDGJfEiSH2Jyiw1lgPyds5wTJwmI0ROxWj3aPMOJXJllDMFP2tw1FJp5sri/AajrL7O04c+O6KgpyzAA8tqeNYymW/eYJKBEL6Xn59qw5jzcaZJZX6DPxm/u89I5kz6SkRBPbTXTnRfUYU+j89trVlfzzOxZF7n/pob28cKjrHGtMD18wFOlYm1dgp9RlPfcKWcpYV5FfUTPTdvzgXjBLNKpFkT/NQ+8gUTDi085hOxeTT6ShcdA8wMKWGKEojvlEfb4+yp0VFNoL4/ac08KcB/lr9A6qeIlFyigYHeO6ss6Jz6cLRRdcALW1s3/9qbBqFaxZo1vdhULTXj3HnIPVYD0zp8hg0YUiTZ8MF3SbIw+FhtJUKGpra+Pb3/42ixYt4sILL+Tee+9leHgYTdNYu3YtP//5z2lvb+eee+5h48aN49Z1uVx89atf5bvf/S6aprFt27Z4DEkgECSYQU+QX7x0jIu++zwf/82bbDvWe8Yyf3yjhW///WASRicAONI5EvFdFvlEgniRjTlFbYNefEH9hLiuUBSrBcljQUl0+zvYnvn739GYwq3oihUkmrEgb02D/a2iq2iuiLWduyQBtnNj2M1GPnKhXmzSNLh7juytZsqOpv5IbtOm+iQVB5NEVmSjJJkD7UM83aiHqZTmWrluTUWSR3Qmd1xUx22bagB9cuo//O6tuFmg7W4eIKDo5/aim+jsrIyxgsw4G9aBAWhtAoeBRkXfBvIYwoF+fj2q2jHLZ8kYUgNgcgO6kNRh8DAk+yMdRarFhOqKjwOFT/EhSzI1efMS300UizkP8tfGTywKecFaCmfLgIpF0/S8oGXL4PzzZ/e600GWdYu76mrd8m6aWI1WHBYHvtBpQpFk1Du1wp1GplxL5CFLf5pZz/3xj3/kyiuvZN68efzrv/4rhw8fRtM03G43n/70p9m9ezevv/46H/vYx3BM0ga2ZcsWALq64jcrQCAQzD2HO4f5l4f3suE/nuVbTxwc551dW5jD1969hO9ev4KxY9jPXzzOL15KrQuvbKGxPVrUWFzmPMeSAsHUWVQaFR2zpaNozHYORD6RILksK48Wz/ZmQeH6WJcQigTJY2VldH/bI+zn5oznDnZGbl+yMHFCEcCHNszDZdMLgQ/vbKU5bPWWCmw92hO5vXl+dglFq6qiVkRCKJobfhLTTfSJi+qwGBPTyTcdJEni/3vXEq5cXgqAJxDi1vu2j8vgnSmx+UTraoVQdDZWxYi2GZdT1NkJAz1gN9Oo6O9zkRy1OBxQzZgmFDE0/ccUM3nLMkBpP+SGS2NKkQviJOr0+/opc5ZRZC+afOG5xhInsUhTAQ2sU7SabWmBoiLdcs5snnz5eJKbC5ddBkYj9E/fDrXAVoBfmaBLyJij51wBDke0ozGvJ/Ed7LNKgP3ABz6AJElomoYkSVxyySV89KMf5dprr8VisUz+BDFMd3mBQJA8ekb8PLG3nb/uauONk2d+OV60oIjbNtdwUUMRcjh40q+ofOWRfQB864mD5NnNvG9tVULHne0ciJltvqRcdBQJ4kNDiQNZAlWDQ52pGf4cb47HdDXUFQqhSJA85hXYcVqNDPsU9rdlfuH6qBCKBElkeYU7cjvjZlKnCN3DfnaHRbhFpU7K3YnN4XFaTdy+uYb/euYIIVXj7heP8a33Lk/oGM7GmFAkSbBxfoLDy5PMqmp35LYQiuLPse4RHt/bDkChw8wHzq9O8ojOjkGW+MH7V9E7sp3XT/Qx4AnywV+9zv9+fAMLSmY+EXJ7U1QoSkQuWrqytNyFQZYIqRq7mzPsvLO9PWwhJ9EY1LelVXJT5OHBkBmzPEEJPRQYZzsHej7Rwqao7VyoOD62c/6QH0mSqHXXIkspYg1pCdvQ9b0Jvk6wlsB0xxbygsEOpinkEw0N6bZz73ynLhYlg7o6vZvpjTcgbxqZSoDT4kRjgjgO2QLhvxssRvodkDcCZd0a7fHKgZois96ySktL+fKXv8yRI0d49tlnufHGG2ck+tTU1HDixAmOHz8+2yEJBII5YNgX5C9vtnDLvdtZ/61n+eqj+8eJRDlmA7dtquHZL1zEAx9exyULiyMiEcDNG+bx+bcviNz/0kN7I+3tgsTQ2BYt4gvrOUG8sJoM1ITFkiOdIyiZGGx6Gid6YjuKRLFakDwkSWJpWPhvH/TRk+HZDWNCkdtuimSJCASJYkGJA0s4FyYbOviSQWzmyJYE2s7FctumGhwWvRj45zdaaB/0TrLG3NM3GmB/+Dx+SVku+Vn2/VfuslLk1GtMu5oHUFWReRtP7n7hGGMxwh+9sA6bOfW6iWKxmgzcc9v5nBcWEPtGA9z0y9fHTSaZDsGQypvhukaZy0plXmIF6nTCajKwKGw7frhrOHPy+saszExBuqVculS9o+N8Q7Q+3aecRShS/bpIJOvrqGgcMg+woC0mnyhOQlGfr5/SnFKKc5JzfDwrlvxwZlGuLhZNV9hQRsBcoNuvnXM5BZqbdbu5ZctmPt54sCBc2wxOzxrOYXZgkk0E1MD4BwwWQI58dt1F+vewPQAjwcTuZ7MSih599FGam5u56667qKurm9VADAYD8+bNY968ebN6HoFAED98wRB/39fOnb99k7XffIYv/Gk3Lx3ujuTcADQUO/jau5fw2r9cytevXnrOcOlPb6kf5yv8yQff4vXjZ2YZCeKPpmmRtvxylxW3PbsuMAVzy9gFg19RaepNHZuWueJ4j7CeE6QOYyHfAPsyuHg97AvSMaR7etcXOZLryy7ISowGOSLMnuz1MOAJTLKGYLo8HyMUXbo4OYUwt93MhzbqNYlASOXnLyZ/IuurMdmvF2RZPhHokyJWhbNRhn3KuPMwwexo7vPw8M5WAFw2EzdvSI96nMNi5IEPr4tYgvaM+Lnpl6+Nm8w1Vfa3DeEJ6MH062rzxfnFJIzlFGlaBp13DgxAVwfYNBrV6LFnoRS1nusLGTFOZD2nKbr1WpgW4wgeWWFBi14z0yRQCmc/STcQ0s85avPqUqebKBZLPuSvBqMzLBZNQ9BXFbBO4Zh/4oTezXPhhXGz8psx8+bpHU09PZMvG4PD5MBqtJ5pPydbQTaBqgtPo3lRUdLnSWxO0ay2rne/+93IcgpuoAKBYFb4giG+/tf9nP/NZ7jjt2/xt30d+JXorIAKt407L57P3/7fhTz1ubdx++ZanNazBPvFIEkSX33XEq5ZVQ5AQFH56ANvZIVdTrJp6fcyHJ7xs1h0EwnizMKS6DZ1KAtyisYyipxWo+hqECSdZVkiFB2LyQYTtnOCZLGi0h25LbqK4kswpPLyYb3gkmc3jculSTQfvaAWm0kvCP5++ym6hnyTrDG3bD0WLURtykKhCIgIRZCB2ShJ5GcvHotMAr19c7SbLh3ItZr49YfXRwT8rmE/N/7iNU72Tk8sip24KvKJJmdVzHEwY/bFzk4Y6gMrNIb071gJlRJ0B5wgNpSJyueqApJBz5cJc9Dcj82nUdWt3w/lOcE0+/2q39dPaU4JJanWTRSLpQAK1oTFoo6piUUhP8hGME9yzO/uBotFzyVypMB1gMUCS5dOO6fIbDTjtDjxKaedV8gm/SecU6S6ozUGeTiNhCJZljEajTQ2Nk55nWPHjkXWEwgEqUfviJ8bf/ka929riggLoPsV37pxHn+5cxOv/PMl/PM7FrG4LHfaM25kWeK716/kogW6n+iwX+HWe3dM+4ROMD0aY0I+RT6RIN4sLI16gh/qyOycIm8gROuAbkNTJ7oaBClArFCUyYVrkU8kSAVWxgR572nJ3P0tGexo6otce1y0oAiDnLzja4HDwgfX6zktfkXl5y8lt6toLJ/IbJA5vyZ5AloyOS9GKBI5RfGhc8jHn95oAfQOnTHnj3TCZTfx24+sj7gbdAz5uOmXr9PcN3WHg+0novlE64VQNCkrY0XbTMnr6+iA4DAQoFHRv2OrpS6M6AV6DzlITHBMUv1n5hNZBqhv1yLF9njYzgXUAKqmUptXm/rNGhGxyAHeVggM6ILa2Qh5wOQc15V1Bj6fLhRdcAHU1MR7xDNn/nxdMPJMz1GlwFYQ6RCLIumCY7ijyJJrjTxiHwzNdqTTYtZbmDaddrI4rCcQCOaOpp5Rrrt7GztPDQBgMxm4fk0lv/nIOl778qX82zXLWDMvb9aFUbNR5u6bV7M67CvcM+Ln5nteT/psvUwmNp9IdBQJ4s3isqhQtL8ts4WiphhRe36hsJ0TJJ/aghxywnkC+1ozd/+LFYrmC6FIkCSWV7gjtzNmJnWK8PzBqO3cJUnKJ4rl4xfVRTKpfvf6SbqHk5MB19zn4WTY1ve8ajd2c3ZOuF1e6Yo4DQmhKD787rWTBMLZojdvmJe21uR5OWZ+99H1LCjRzw1aB7zc9KvXaBuYPF8spGpsb9KFooIc8zlt9AU69cUO7OHzzt3NE0yYGBycth1XUtE0OH4czEFAolHRayXLpJORRYZUC5YJ84kCYHID+uehoXHcNMTClugioTgIRf3efkocJZTmlM76uRKCpQAK10PuYr3jyt8F3jYIDoJ6muiheMBSCmcTwMb+P8uXw7p1cz/26VBRof9M137O7ERjAk3EYAP07+QCo5nhsFaU35dmQtFMETNwBYLU4q1T/Vx797ZIvkhJroU/37mR771vJRc2FGE0xPfrwm42cu9t50dO6Jr7vNxy73Z8wcR+CWYLB2I7ioRQJIgz1fl2XDbdfnJ3y0BGTwY53i3yiQSphSxLLC3XL0JbB7z0j2Zmbsq4jiJRyBEkibrCnIg1UyZ38CWDZ8NCkSwRcR5IJsVOKx9cr+e1+IIqv3w5OV1F22Js57Ixn2gMp9UU+e4/0D4krhlnSUBR+f0OPX/FIEvcvrkmuQOaJQUOC7/76Abmh8/Nm/u83PjL1+gYPHMiavewn+cOdvKjZ47wkQd2MOzTux1EPtHUMMhSJB+zdcBL1/Bpn3FjI7z8chJGNkP6+6GrE0wePLKT44q+DW00Hoss0hs0Yjojn0jTf0zR68F+2a/nE7VGr4WVYveshhdUg4S0ELXuNOgmisXshrzlUHoJFF0AzgZAAn9HWDQaDlv3SWA9Rydfby+43XDxxWCaPO4iocgyLFsGw8PTymRyWhyYZNOZXUWyJXxDw4qBlmL9+8iZ4AjohG9lPWGlLSdHFFcEglTh7/s6uPEXr9EXLi4tLHHy8D9sjhSe5gq33cyvP7yeCrcNgIMdw/x1V9ucvma2MmY9l2M2UJ1vn2RpgWB6SJIUsSHoGQlErNkykePd0WJ1nShWC1KEcTlFGZr7dyy879lMhsh5g0CQaGRZYlmFPuGmfdB3ZoFMMCNO9o5GJmKsmZeXMp0Nd1xUhzncVfSbV0/SM5L4rqKtR6P5KdmaTzTGWE6Romoi43aWPNXYEemSu2JpCSUxNkfpSpHTwu8/toHacMf/yV4PN/3yNZ7c38F/P3uEjz7wBhu+9Szn3/UMH77/DX74zGFeONQdWX9DXUGyhp52xGaG7Zmoq6ilZdp2XEmjqwsGOsAc5KBWiha2mFshRzuK+hQTptM7ikKBsO1ctLbdYhpB0jQWtOmigWozozpmt2/1efsozimm1JEm3USnI5vBVgp5K6HkEijcDDl1QEjPMTLYwXQOS9WuLlixAoqT32k8IbW14HLB0NRdHXJMOdhMNnyh084hDVaQjBGrvr7C5HQQx0UomqrqPjo6yv/8z/8AMH/+/Bm/3t13382KFSvIzc0lNzeXjRs38re//S3yuM/n45Of/CQFBQU4HA6uu+46Ojs7xz3HqVOnuOqqq7Db7RQXF/NP//RPKMp438QXXniB1atXY7FYqK+v5/777z9jLD/5yU+oqanBarWyfv16tm/fPuP3JRAkg/u3nuDO372JX9FbHDfNL+CPd2ykPEFFmFKXlR/esCpy/6nGjoS8bjYx6A3S0q8X7heV5SIn0fNdkLmsqowWqjPZEuR4T7SjqFZYzwlShLHCNWRml4NfCUWyDOuKcsRxTJBUVsYEee8VOUVx4bkUs50bozjXyk3r9KwibzCU8K4iTdMiHUUOi5GVlXM7iS/VWRW2LQciVumCmfGbV6NF8Js3zEviSOJLca6VBz+2PjIx8njPKJ/4zZv84OnDPHOgk44JrO6dViNXrSjj+jWViR5u2jJpTtHgoF7gTwfa20EZBSlEYyja2TJPagVAQ2ZANWA+XShS/Xo2kRwVglqMo1T2gD08p0ApcsEsutRCWghFVah112E4o6MpDTFYwF4OBauheAsUbgL3EjCepfY4NAQ5ObB0aWLHOR0KC3WxqLt78mXDmAwmXBYXvuDpQpEZZFNEKPLmJUcomtar1tXVTfj3yy+/HNMkLWB+v5+uri5UVUWSJN797ndP56XHUVlZybe//W0aGhrQNI0HHniAa665hp07d7J06VI+97nP8fjjj/OnP/0Jl8vFpz71Ka699lq2bt0KQCgU4qqrrqK0tJRt27bR3t7OLbfcgslk4lvf+hYAJ06c4KqrruKOO+7gd7/7Hc8++ywf/ehHKSsr44orrgDgD3/4A5///Of52c9+xvr16/mv//ovrrjiCg4dOkRxqqqdAkEYVdX41hMH+NUrJyJ/u/a8Cr593YrIzLlEsXZeHkVOC93Dfl4+0oMnoGSt//ZccLA9Np/IeY4lBYKZE3vxvrt5gHetKE/eYOaQsY4iSRJCkSB1WB7TUbQ/A3OKmno8qGFHh3qRTyRIMitihKLdLYNcurgkeYPJEGKFoksXpdbnecdF83nw9VMEQiq/efUkn3jbfPJzEtPxdKhzmJ4R3fFhQ11+3K3A043YLoZMnpQ01xzuHOb1E3ouz/yiHDZmWCdNmcvGgx9bzw0/f+0MlwOn1ciychfLK10sr9B/qvPtYgLKNFk52b44NASdnVBTk6ghzYxIPpEXZDONPr1WYsVPrtYPQFByEtI0jKfbvmkKmMaL9y3Gkbjazo0GPTjMDkpy0rC+PNZRZj+Lm43RCsZJxNn2dli1CsrK4jq0uLNoEezZA6EQGKYm6OXb8mkZajntrwa9wyo4CNiQXGYg8U4t06rENjU1nfE3TdNobW2d1otu2LCBL37xi9NaJ5bTRaa77rqLu+++m9dee43KykruueceHnzwQbZs2QLAfffdx+LFi3nttdfYsGEDTz31FI2NjTzzzDOUlJSwatUqvvGNb/DP//zPfP3rX8dsNvOzn/2M2tpavv/97wOwePFiXnnlFX74wx9GhKIf/OAHfOxjH+P2228H4Gc/+xmPP/449957L1/60pdm/P4EgrnGFwzx+T/u4om90e6dT11SzxcuX5AUX15ZlrhscQm/334Kv6Ly0uEe3rEsTVtrU5AD44QikU8kmBtiZ1hPGGyaAWiaFukoKnfZsJoyYGaXICOoK3JgMxnwBkMZ2VEk8okEqcSKmK6OPRPNpBZMi1G/wuvH9aJ1hdsWyS9NFUpdVj6wropfv3oST0DvKvrndyxKyGu/ciSaT7RpfnbbzoFujz52rBMdRTPnt69Fu4k+tGFeRubyVObZ+eMdG/np80exmw0sr3SzvMLFPCEKxYVyl5VCh4WeET+7m/V82nHbUSCgCzDr1ydvkFOhrw+6WsHiB2MujUG9VtIgtSKhCz4+cjgjfUZVQDKMs50D3XpuXUusUDS7LlBPcJTK3ErMxtSwY50yg4PQ1gaqCosX61k+08Xj0UWXFStm1ZWVEGpqID9f356Kppax6DDr5zoaGhIx789oh4BuOeuwWvCZgARH8k1LKLr11lvH3X/ggQeQJImrr74at9t91vUkScJqtVJWVsamTZvYsmVL3A5GoVCIP/3pT4yOjrJx40befPNNgsEgl112WWSZRYsWUV1dzauvvsqGDRt49dVXWb58OSUl0dlKV1xxBXfeeSf79+/nvPPO49VXXx33HGPLfPaznwUgEAjw5ptv8uUvfznyuCzLXHbZZbz66qtxeW8CwVww7Aty+307eOOkPkPCIEt845pl3LS+OqnjunypLhSBbj8nhKL40RgjFC0RQpFgjihwWKjKt9Hc52Vv6yBKSM24ma89I4FI4G1dkegmEqQOBlliSXkub57s51Sfh0FPEJc9xQJfZ8E4oUh0FAmSTGWejTy7iX5PkL0tg2cWyATTYtuxXgIh3QL74oVFKflZ3nnxfP53ezOBkMqvtzXx8QvryEtAV9G2Y9F8ogsahFBkNMisrHLx2vE+Wge8tA14E2aXnimM+BUeekuf6G0zGbg2g+3WKtw27nrv8mQPIyORJImVlS6ePdjFkE+hqdcz3mlBkqCjA0ZGwJHC521dXTDQCW6VkGzjoKJ3FG02HI0sMqxZMUinXdOq/nA+UbRbxi+F6DJ4aQjnE2myRKhgdm4uITVEoT3NvvuHhnSRaNMmXSxsa4PKGXzPtLfrlm6p3pUG+ja+eDFs3TotochsNBMIBbAYLNEHYm6XhUycKoLy5ngP+NxMSyi67777xt1/4IEHAL2jZ8mSJfEb1RTYu3cvGzduxOfz4XA4ePjhh1myZAm7du3CbDafIVyVlJTQ0aF3T3R0dIwTicYeH3vsXMsMDQ3h9Xrp7+8nFApNuMzBgwfPOm6/34/fHw3BHAoHXgWDQYLB4DQ+AYFgZvzqpWMRkchuNvCjG1Zw8YKipG9/51e7yDEbGA2EeO5AF16fP+OKzMmisU3/npEkqCuwJv1/LchcVpS7aO7z4g2GaGwdyDirw8MdA5HbNQV2sS8JUoolpQ7eDB/fdzf3ZpSVzOHO6ISHefniOCZIPsvKc3n5aC+9owFO9gxTIYrVM+bZA1GHg7c1FKTk/l1oN3L9mnIe3N7CaCDEL146yucva5jT1wyGVF4/rgtFRQ4zNXmWlPxsEs3aajevhTvQXj3azdUrU9ySKMX4yxvNjPj1SU9XryzDZkBsV4IZsazcybNh29C3mnqpdIXFc1XVC+fDw7pIUFubxFFOQmsrqB4wmDiquvFpulvEWmM0j64nZMNkNKPKMROwJMBSCHJ0wkCLcQRzUKM8rO+H8p2o5plPKAiGghiNVhy2PNJmDx0Z0f/nmzfD296miyb/9396h9l0PotAABQFVq7U7dxCCW6pmQm1tbBjx5Tfq9mcg9nkwKsqmEwx9nzGHDBYQZLJ0UwcKZEpb07s+59VCMjXvvY1gKTk8SxcuJBdu3YxODjIn//8Z2699VZefPHFhI9juvzHf/wH//Zv/3bG359//nnsZ/NuFAjiyNMHZEAXYD7W4MdzdAdPHD33OoligVNmZ6/MgDfIT/74JA2uM5p8BdMkpMHBdgMgUWTReOGZp5I9JEEGYxqWAP0E+3d/f4VNJZm1D2/rjL4/T8cJnngisaHWAsG5CPVEt88/P7ud/oOZs//tOqYfx2Q0Dmx/iSNiHokgydi80fPpB/7vBVYVZM7+lkg0Df6+W9+/jZLG4OEdPHEs2aOamAYFDJKBkCZx7yvHqRo9Qs4cNm4eH4LRgF6uqbb6+Nvf/jZ3L5ZGqIPRY91fXt6NsXVncgeURmga/GyPvr8BVAeaeOKJpqSOSZC++Aei++JfXzltX1y7Vv994ID+k8pc8B4A3uyRIByXV2nqZUydaV95B5rJRe/Ea0c45N9B9ZEdjJ2ijtYupHfBJbMamgyklV+V3Q7FxeDzwVPhutN5583sec47T+9IOp5G19vLp9nBuEyP1Tlz24rG7fSNfh/oOGOJuSQuQlEyMJvN1NfXA7BmzRp27NjBj370I2644QYCgQADAwPjuoo6OzspLdWtrEpLS9m+ffu45+vs7Iw8NvZ77G+xy+Tm5mKz2TAYDBgMhgmXGXuOifjyl7/M5z//+cj9oaEhqqqquOSSSygoyJyZn4LU5XsHXwa82M0GPnnD21PK3kGpaGfnn/cCMOyq5corE+P/nckc6RxBeW0bAGvrS7nyypVJHpEgkyk52c8jv9oBgJpXzZVXLk3yiOLLnr8fguO6r/u7LlrHBfXiuC1IHeZ3DPPgT/TLyZCrgiuvXJHkEcWHkKrxxR3PAirzCnK4+l0XJHtIAgHmA1089eAuAEwl87ny8gXJHVCacqhjmIHX9O+tTfWFvPfda5I8onNzyNDI/+5owR+SaHUs4LOX1s/Za/3Pc8dgv66aXX/hcq5cXTFnr5VOXBxQ+MXB51FUjU7VyZVXbk72kNKGHU39tL+mn6evrnbz8fetS/KIBOnMJk+Qnx14HoAhUx5XXhnOI9q+HZ55Bmw2KCuDD3wgiaM8B729cO9PIXgAXEXsG1kC6N1PtcETAKiSlcCOe3EYrVgM4fK5GgAtCLmLdPu5MP2Og9R2RieNGIwjFBx+fsbD6xjpYH7efJaVLJvxcyQMjwdOnYLzz4ctW8AUM4vi6FF46CEoKYGcKVi3qyocPgzvehesWjVnQ54TXntN3/YXTa2OebTvKPs691HmPK0zdugQqF4wOFDsXtQEl2xnJRSlEqqq4vf7WbNmDSaTiWeffZbrrrsOgEOHDnHq1Ck2btwIwMaNG7nrrrvo6uqKdEM9/fTT5ObmRiz0Nm7cyBNPPDHuNZ5++unIc5jNZtasWcOzzz7Le97znsgYnn32WT71qU+ddZwWiwWLxXLG300mEyZT5njJC1ITvxKidcAL6Pka5lm0ws4Fly0tw/jQPhRV45kD3Xz96mUpJWSlI4e7PZHbSyvc4ntGMKesrC7AIEuEVI29rUMZt72d7PNGbi8oc2Xc+xOkN4vK3ViMMn5FpbF9OGO2z/ZeD35Fzy+pL3FmzPsSpDera6ITBfa1Zc7+lmhePtYfub1lUUnKf46f2tLAn99sRVE1fv3qKT7+tvo5y4N77UT0s3nbwtT/bBKFy2RieaWLnacGONY9yqBfpdBxZn1FcCa/f6M1cvuWjTVimxLMiiKXiZoCO029Hva3D4NswGSQQZb1Yr/TqWcAeTzgciV7uGfS1wf97ZAfBNnAwaBumV7IIBb0GooiOQkqXswGE7IaFoGCw2ByAkZQo5ZgrcZhLu2KCkVqXg6yOjPLMA0NLRSg0Ooi5fdSjweammD9erj88jNt1xYu1PN7du+emoDS2QmFhfo66fYdVV8Pr7wy5WwutykH1CCoQWRi7BIkEwT7QLJgzDXxiU8b4DNzOO7TmJJQdOrUqcjt6urqCf8+E2Kfazp8+ctf5p3vfCfV1dUMDw/z4IMP8sILL/Dkk0/icrn4yEc+wuc//3ny8/PJzc3l05/+NBs3bmTDhg0AXH755SxZsoQPfehD/Od//icdHR185Stf4ZOf/GRExLnjjjv48Y9/zBe/+EU+/OEP89xzz/HHP/6Rxx9/PDKOz3/+89x6662sXbuWdevW8V//9V+Mjo5y++23z+pzEQjmipO9HsaOb3WFqRcq6LKZ2Di/gJeP9NA64KWxfYil5Sl4UpFGHGiP5josKctN4kgE2YDNbGBRqZP9bUMc7hxm1K+QY8mYOSkc7x4FwGqSKcu1Jnk0AsF4jAaZxWW57Goe4ETPKMO+IE5rml1gTcDR7uHI7fri1Dt3EWQnJblWSnItdA752ds6iKpqyLKY3DRdnj/UFbl98cLE29lPl8o8O9evqeR/dzQz7Fe4d+sJPvf2+HeTjfoV3jqlC0W1hTkiA+s01tXks/PUAABvNPXxjmUip2gyuoZ9/H1fOwD5OWbeufzsLjgCwVRZWeWmqddDQFE51DHMsoqY2k1uLnR06IX/VBSK2togMAiyCU2DxrBQtNYQtTrzSw5CmopBjiniawqYxr8fFY1W4yg1HXqxTQNC+TM/Z/UpPixGCy6re8bPkRA8Hjhx4uwiEejC4YYNemdRXx/k55/9+TRNX+Yd75iS0JJylJZCVZXeXTWF8eeYHViMFgJKAKsxprZgtAL6JLlSxUTAlNjzyylVb2rD4WOSJKEoyhl/nwmnP9d06Orq4pZbbqG9vR2Xy8WKFSt48sknefvb3w7AD3/4Q2RZ5rrrrsPv93PFFVfw05/+NLK+wWDgscce484772Tjxo3k5ORw66238u///u/j3tvjjz/O5z73OX70ox9RWVnJr371K6644orIMjfccAPd3d189atfpaOjg1WrVvH3v/+dkpKSGX4qAsHccrx7JHJ7flFqfvFevqSEl4/0APB0Y6cQimZJY6xQVC6EIsHcs7LKzf62IVQN9rYOsqEuM+zZgiGVU3367LKaghxREBSkJMsqdKEIYH/bUEbsf0e7oucu9Sl67iLITpZXuOkc6mTYp9DUO0qd2D6nxaA3yJsndTGkrjCHmsIpWNKkAJ+8pJ4/v9mComrcu/UEH7mwltw4i/Lbm/pQwrP7Ngub2zNYV5vPz1/Si7mvnxBC0VT4445mgiF9m7rh/CosRkOSRyTIBFZWunl0VxsAu5oHxgtFY+JKRwcsSDF7VlWFowfA4gNjPt2qhV5Vbxq40HAkstioakMipitIUwEZDOPz5XsNPgIoVHeHnz7XDqaZT5b0BD3kmnNxmFP4uOj16vlB559/dpFojPJyWL0aXngB3O7otnE6fX2Ql6d3E6UjkgRLl8KhQ/o2drb3GcZhysFqtOJTfOOFItmKniWnUeMzc1NLDv/fnA58PFOKgtU0LfJztr/P5Gem3HPPPTQ1NeH3++nq6uKZZ56JiEQAVquVn/zkJ/T19TE6OspDDz10Rm7QvHnzeOKJJ/B4PHR3d/O9730Po3H8jnzxxRezc+dO/H4/x44d47bbbjtjLJ/61Kc4efIkfr+f119/nfXr18/4fQkEc82x8Gx40K3nUpHLlkSF1qf2d55jScFUGOsoys8xU+wUtgyCuWdVpTtye6xgnQmc6vNEijapKrQLBMtjLtD3tQ4mcSTxY5xQJDqKBCnEysro/rY3Q/a3RPLKkR5C4eNqOnQTjVGVb+facF7QsE/h/q1NcX+NreFJcwCb5xfG/fnTnbXz8hlzJ99+oi+5g0kDlJDKg6/rbkCSBDetm5mzj0BwOiur3JHbuye67svJgWPH9E6RVKKvDzpPgU0Dg5394W4igGWGqHPWgGrGEBuFoAZBNoJhfF2lxThCeS+Yw5pSKN/JbPApPoodxcjSlEr2icfn0/+va9fq3T8TxKucwdq1ek5RR8fZl+ns1IWWgjSeIFFbq4thg5OfF8qyTJ4tH1/IN/4Bg0XfzlQFhyqzejCxdbwpSZz33XfftP4uEAhSk2MxHUWpKhSVuWysqHSxp2WQxvYhmvs8VOXbJ19RcAZdwz56RgKAbjsn8p4EiWBVtTtye8ILhjTlRBoI7QJBbBduJgpF84VQJEghlscIRbubB7lmVUUSR5N+xNrOXbKoKIkjmT6fuqSBv7zVSkjVuOeVE9y+uSauVp9bj/UCelF/4/w0LpjNES67iUWluRxoH6KxfYghXzDuXV2ZxHMHu2gb1AuRly4qFtfWgrixtDwXoyyhqBq7WwbOXMDlgp4eGBjQO0VShc5OGOqGIiPIBl71R79n59EWviXRFZQwSTFlc00B2az/xNBiGqG2NSqGhQpmfr6qaiogkWdNoc8rllBIt5FbvVoXiaxTtGN3u3ULur/+FYqL4bRGDYaGwG6H5cvjPuSE4nbrWUW7dk1pm8+zumnqPzH+j7JZzylSgyAn/tg2JaHo1ltvndbfBQJBanI8ttCZghlFY1y+pIQ9LXqB6+nGTj58wcxtLrOZxrao7dzistnNahEIpsr8Igc5ZgOjgVBGCUXHe1JfaBcIFpQ4MRtkAiGVfTHHgHRF07RIN3SZy4ojgzLPBOnPipgO2j0TFcgEZ0VVNV4IC0V2s4F1tefILEhBqgvsvPe8Cv78ZguD3iAPbGviU1sa4vLcPSP+iCPAsnIXbvs57HyymPW1+RxoH0LT4M2mfi5ZlD5daYnmN6+djNy+ecO8JI5EkGlYTQYWlTnZ1zrEka4RRvwK46pMDge0turCTCoJRe1tEOwDgwNNg8d9ugOVGQWXpvvHabKDUSWE2RBj06gGwexGtwWL0mIcZXlnVChSZtFR5Al6sZtsqZtP1NYGlZVwxRVgm2Z+3vLlsHevnuFTVzf+sY4OWLZMt6lLdxYuhDffBEU5UxA7jRyTvseoqMgR0zcZjHYI9M/xQCcmRfvYBAJBvNGLLXqhs8Jtw2ZOXV/iy5dGrSKfbhT2czPlQHs0AFzkEwkShUGWIsWztkEfXUO+c6+QJqSL0C7IbsxGmYWl+sXpse4RRv0zywNNFXpGAgx6g4CwnROkHvk5Zqry9SLJ/rYhlJCa5BGlD/vaBiNd75vrC9MyL+VTl9RjCOcV/uKl4zSHcwxny6vhbiLQPxvBxMSKi9ubhP3c2TjRMxrJ/63Ot/O2hvTq3hOkPivD132aBntbTutmH8toaWsjZVBVOLgbLCEw5rAr6KI1pB/L32s5GMkkUuRcgmoIkxxb6FfBcKY40mIcoSambDUb6zmv4sFtdWM3TVOESQSBAIyMwMaNugg4XaxWvasoGARPzDHT69V/r1wJmeCCM28eFBVBb++ki9pNNswGM8FQcPwDxhzQQhOvNMcIoUggyBJ6RgIM+/SCUarPhm8odjCvQG+J397UR/9oIMkjSk8a22M7ioRQJEgcsX7VmZJTFCsU1ab4d6gguxkLEta08ceBdGSc7ZzIBhOkICsq3AB4gyGOxlg8C87N8we7I7cvSaN8olhqCnN473m63eCQT+Fjv34DT2D24vzWozH5RPXCdu5snF8TIxSJnKKz8rtx3UTVyHIGFGEFKcW4nKKJumudTjh+XBdoUoHeXuhpARsgW3jMWxZ56BrzgchtP04CqoI5IhRpgATyeKs1r6TQY/BSE+4oUm1mNPvMM2X8IT8lOSWTL5gMmpt1W7UlS2b+HAsXwuLF+nON0d6udxjVZoiTkM2mf0Z9kx+bbGGhKBA6reYZsTdMfL7XrISiUCjESy+9xEsvvcTgFIKaBgYGIstrqRZmJhBkOMdj84kKU7vIKUkSly/RD44hVeO5g12TrCGYiDHbCrNBFgU2QUJZlYlCUY8uFBU6LMIHX5DSLKuITgxI95yi2MK76CgSpCIrYnKK9pw+k1pwVmLziS5emL4dDv/fVUuoDV9XHewY5h//tHvWdY6tx3ShyGyUx4khgvEUOS2RyY97WgbwBpIz8zqV8QZC/OnNFkDfnt63pirJIxJkIrHXfRPajrvdujgzhaJ5QujshP4OsFtRkXjCq9edTKislqNZMV7sqGjIYx0uqgKSEQzjRaBW4wgFQ+AMm2jMpptIURUMshGX1TX5wolmdFQX+zZsAPMsLFENBr0jyWKBwUG9uygQgPPO0x/LFOrrwWQC37ndVcwGMzaTbQKhyAqSAdTEH9tmJRQ98sgjXHzxxVx33XWYTJMXTcxmM9deey2XXHIJjz/++GxeWiAQTJOxIiekRxi0sJ+bHb5gKCIONpQ4MBlEA6kgcayabGZZmjHkC9Iz4gdSvyNTIFheEb243JvmQtGxLiEUCVKb5eOEooHkDSSN6B3xR84NFpU6KXenoL3OFHHZTfzyljWR/LQn9nbw4+eOzvj5TvV6aO7TLXjWVOdhNWVQ0WwOWB+2nwuGNHY2JyfLIZX5vz1tEfvWd68oJy9H5F0J4s9YPi2cRSjKydFFhs4Uqem0nARlAEw57Ay6aVf1Y9AFlh6satQqbFi1jk8iUoMgnykUNZtGqemKVz6RhxyTPTWFolOnYOlSXQCZLdXVsGoVtLTo3UTl5dAQn5y/lKGyUn9fXZNPendZXWcKRQYLSCbQghOvNIfMqnL48MMPA/C+970Pu90+6fJ2u50bbrgBTdP4y1/+MpuXFggE0yS22JIO+Rqrq/MoCJ/Mvni4G19QzBKbDoc6hlHD5ytLhO2cIMGUuqyU5Oon0XuaB1HV9O4ijrWdmy+EIkGKs7DUiTFsLbO/Nb2t52KFrgYhFAlSkOUVroidvugomhovHelmrOnm4jS1nYulvtjJf9+4KrIdfP/pwzy1v2NGzzXWTQRwQYPIJ5qMcTlFwn7uDH4bYzv3oY3zkjgSQSZjkKXIpIm2QR9dvtMs5iRJzypKhZwiVYXDu8GmgSGHx7zRyclX2TogNCY4G+lTJIxSjFivKWCwc3oZ/Yx8ooKZC0WjiocCeyFmQ4qJuv39uuC3fn00d2q2rFsHBQX6c69Zo3cYZRIGAyxapIukk+A0Owmdnkc01r2mJj5vdlb/4R07diBJElu2bJnyOmPLvvbaa7N5aYFAME1iO4rSYUa8QZa4dLF+8egNhnjlSM8kawhiEflEgmQz1lU07Fc43pPeuQ2x1p21KW7dKRBYjAYWlOgXqUe6htPWjmfEr0RmptYV5VDgyLALSEFG4LSaIpbOB9qH8Cvpub8lkudi8om2LEp/oQhgy6IS/umKhZH7n/vDLg51DE/7eV6JySfaNF/kE02GyCk6O7ubByLi9fIKFysrU7BDQZAxjMsp6p+gsO10wrFjEEryMbKnB7qaIceIKhl5IiwUmVF5u6UV1PA1n8HNUNCHKZJPBGghMJ15HdhiHKG2MzopMpQ/84lNIVWhyJZikwQ0TRf5zjtP75KJFwUFuvBUU6PnFmUiRWFr3UnyuWwmOxIS2ul5RMYcXaBMMLMSiprD4VO10wicqqmpGbeuQCBIDGOFTrvZQGmudZKlU4PLl0RneDzVOLOZednKgRihaEm5EIoEiWfluJyi9J5lHTtLvKF45rPEBIJEMWY/p2pwoCM9u4p2NPWhhLsRRcFUkMqsqHQDuv3VTMSBbEIJqbx0WBeKnFYjq6vdyR1QHLnzovlcvbIcgNFAiI/9+g36RwOTrBVFVTVePabbHjmtxnE2ooKJqcyzUxG2LnzrVD8B5dzFuGzivq3RrJUPbZiHJEnnWFogmB2rwsdBgN0DExS2XS69c6QnyZN/OzthsA1yHLwZcNOp6nWxt1l6cGnRsYVkF75QALMca/+pgTx+0pKKRqtplJqwUKQZDai5k7ttTYQ/5MdsMKee7VxnJxQWwtq18X/u88+H66/XhcRMpLAQHA4YOfekWbvRhlE2ElRPs5kz2OB08SgBxKVnbDqBjWPLKkriVTGBIFvxKyGa+3W/69rCHGQ5PU4UL2goxBb25n72QBehNLevSiSNbTEdRaVCKBIknlXjhKL09o3f0aTPUpUk3RZTIEh1llVEv/f3pWlO0VjBFGDT/BSbXSkQxLBiXE5Reu5viWJX80AkM+VtC4owZlCGpiRJfOe6FZHv31N9Hj71+7dQQlMTLw50DNEXFpY21BVk1Gczl4zZz/mCatrn8sWL5j4P/7enHYA8u4l3hwVMgWCuWDFZR5HdDh5P8nOKTh2GkAeMDh73lUX+fJWtHZTo9WpAchBUQ9GOIlUByQDy+AnXXQYvZl+I4vBXTyjfATMUZT1BDznmHFyWFBKKFAV6e3WbuPz8yZefLiaTLqZkKm63/jOJUGQz2TAbzASU04Qi2QLIMA3NJR7M6uyjKNxGdfDgwSmvM7ZsYSZvDAJBinGq1xMRWeqK0sfj32oycNEC/XumdzTAW6fSu9icKFRV42B4RmuF24bLbkryiATZSGxuw+407iga9gUjHXoLS5xifxKkBctiZqKnq1C0LSarY0Od6CgSpC7jhaKB5A0kDXj+UDTU+ZIMyCc6HZvZwC8+tJZCh54vsfVoL998/MCU1t12NCqOX1AvaiVTReQUnckvXz4eufa/fXMtNrNhkjUEgtlR7rJSGLYI3j0QOrOuLUlgNEJLS+IHN0YoBIf3gB1CspUnvCUAmAlxmbULQgORRb04CKgKprGOIi0IkknPjImhxTTCvK7om1XyZ94Z4w16Kc0pRY5XBlA8aGuDqipYtSrZI0lPZBmqq2H43N3mFoMFq9FKUD2tC9lgBtmQcPu5WW2B559/Ppqm8etf/3rK69x///1IksTq1atn89ICgWAaHIsJYq9Ls3yNty8pidyeaTBsttHc72HErx9MRD6RIFk4rSbqw8L0gfYhfMH0zG1482Q/Y82MscUIgSCVWVyWiyHcPby3Nf2s5wY8AfaHO2OXlOWSn5Niob4CQQxLylyR/U10FJ2b52PyicYmg2Ua5W4bP7t5DSaDvk3cv62JP+44t+2+pmnj8ok21wtxfKqMF4p6z7FkdtA97OcP4e0tx2zglo3zkjwiQTYgSRKrqvRJE0NBjSZtglzJ3Fw4cQKCwTMfSwRj+UQOK28EC+gK285dZO3BKYcgFJ2UPKLpj8ljsx5VRReJJOO4p2wxjlAT0yQVKpiZUKSiogFuawo5V/j9ehfYxo16R5hgZpSUTJrNJUkSLqsLf8g//gHZrP+oia2jzEoouv766wF49tln+f73vz/p8t///vd57rnnAHjf+943m5cWCATTIDZIfn5x+nQUgR5yO3bx/VRj57SsLrMVkU8kSBXG7OcUVYsUfdONMds5GB+aLBCkMlaTgYbw8f5I53DaCbWvHe+NzEYV+USCVMdmju5vhzuH8QbSa39LFB2DPhrD56grKl0UOScoJGYIa2vy+cY1yyL3//WRvWw71sORzmGeO9jJA9ua+OZjjXz812/wzh+9zIqvP8WL4eymklwL89PIASLZ1BXmRDq43mjqz3qr8vu3ncAfzmq6aX01bruYaCFIDCtjc4q0Cb7DXC4YGIDu7jMfSwQdreF8olwe90azsN9l7dCtvcaEIsnG6OmHcU0B05kiUItxhNrO6HdOaIYdRT7Fj9VoIc/mntH6k6JpejfXkSOTdrdEOHUKGhpg8eK5GVO2UFgIZjMEzp1ZmGvJJXSGICSDIQe0NBKKbrjhBlauXImmaXzxi1/k+uuv55VXXhmXP6QoCi+//DLXXXcdX/ziF5EkiWXLlnHzzTfPevACgWBqHOtK346ivBwz68LF2ZO9Ho50ndvfUzA+n2hJWYYGAwrSgpWxftXNA0kbx2zYcSI6u0x0FAnSiaXl+sxORdU41DHFi8IUYVtsPpGYWS9IA8YKZKoG+9tEV9FEvBBjO3dxBtrOnc4H1lVza7ibIxjSuOmXr/P2H77Eh+9/g6/9dT+/euUETzV2cqB9iGF/tH6yZVEx0gwzLrIRSZIi52fDfmXchLlsY9gX5NevngTAZJD4yAV1SR6RIJuIve7bpU1Qc7LZwOdLXk5R034gQMjo4AmfLhSZCXGptQs0L2jhQr4xj6GgF+O47iEtnBcznhbTaMR6TpMkQu6Z1do8wVGcFic5pjmo1Xm9cOAAWK2wZIneWXXgAAye41xlZES3C9ywQc8REsycwkJwOicV6GzGs3RtGe1AGglFkiTx8MMPU1ZWhqZpPPzww1x00UU4HA7Ky8spLy/H4XBw8cUX88gjj6BpGmVlZTz66KPi5EcgSCCxHUV1ReklFAFcvlTYz02HxvboQWhJWQqFIQqyjlWxFwxpKBT5lRC7wnkT1fl2SnKt515BIEghlldEO0rTLeB7TCgyyJLo5BOkBSuqYnOK0mt/SxSx+URbFmW+UATwlXctYeMkGWsGWaI6387m+gI+vLmWf7x8YYJGlzmsizlOxHaCZxu/e/0Uwz5ddLz2vEpKXeK8VZA4YvP6dqln6Yo0maD53Facc0IoBEf2g83A60opPaou+lxi7cYhh0CJTgzUZDfDQS/mSD5RCL2zY/z+NCoFGdZ8VIZdQ1WXHYwzywPzK35Kc0rjXyfv6ICmJj1j6MYb4brr4EMfgrVr9e6uAwegf4Ic8OZmWL4c5s+P73iyEZsNiosnFYrsJhsG2YCinpZHJFsA44TrzBWzfrWamhp27tzJHXfcwaOPPoqmaQQCATo6xhdzJUni2muv5ac//SnFxdlxYigQpAKapnE8nFFU7rJiNyf2SyYevH1JCf/2f42Abj/3qS0NSR5RajM2k85hMVKZZ0vyaATZzMJSJxajjF9R2Z2GAd97WgYJhO07RLFakG4sj7lgT6cOh64hH0fD3cMrKl04rWImoyD1WVHhjtzek4bHu7kmoKi8ckSvphXkmFlRkR0TmUwGmZ9+cDVfeWQfHUM+KvNsVOfbqcqzU5lvoyrPTpnLitGQQuHlaci62qgYt/1EH7dvrk3iaJKDLxjinldOAHojwCcuEt1EgsTitpupK8zheM8oe7QcjvlGmW9Vxy/kcunChd8PlgTaj/b0QFcT5DrG2c5dZQvXrZWoHV5QduJXPZgM4bqZqoBsOqOjqMU0QlUPGMNvUZlhPlFICyEh4Y6n7VwgAMeP67lQ7343nHceGMPvp6pK/1mzBnbtgv37ob1dz9LJz4e+PnA4YP16/ctEMHvmzdNFuXNgM9owG8wEQgGMckzN1mABKbHnCHGpGBcXF/PQQw9x+PBhHn/8cXbu3ElPj34iWFhYyOrVq7nqqqtoaBDFXcHc8ac3mvnh04cpcVlZOy+PNfPyWVuTR6Ejc/2vp0LfaIBBrx4YWJemfteVeXaWlOXS2D7EnpZB2ge9lLmEADIRnoBC64AXgIYSB7IsDu6C5GEyyCyvcPHGyX5O9nroGw2kVSj99hPRWanralMoXFQgmAKLy3KRJd0KK506il49HmM7J/KJBGnCwlInZoNMIKSKjqIJeKOpj9FwdtNFC4qy6vw0L8fMTz64OtnDyGgWljrJtRoZ8ilsP9GHpmlZ52Dzl7da6B7Wg9Dfuaw0ba/7BenN+9ZW8Z2/H0RF4iddZn5Q7Ru/gMuld6t0deliRaJoOgRDPSjF1fy9T3ersUohLrWEBaJgtMvJKxUQUIfIMYbriFpQF4nk8ROXmo0j1DTPPp/IG/RiN9lxW90zWv8Ment1e7+FC2HLFigvn3i5igr9Z+1a2L0b9uyBxkZQVX29srL4jEeg289Jkv7ZyhOLPlaTFbPBQiAUwG6KsaGTLSCnWUdRLAsWLGDBggXxfEqBYEo093n410f2EVBU2gZ97Dw1wC9f1mfU1BbmsGZeHmvn5bG2Jo/5RY6sOnE81h2TT5SGtnNjXL60JBKA+3RjJ7dsrEnugFKU4zH/bxGEK0gFVla5eeOk3tK+u2WAS9IolyBWKBIdRYJ0w242Mr/IwZGuEQ51DBNQVMzG1J+1vu1orFBUmMSRCARTx2yUWVzmZHfLIMd7RhnyBckV3XARYm3nLs4S2zlB4jDIEmtr8nnuYBe9owGOdY9SX5w910FKSOXnLx6P3L/zovokjkaQzXxo4zx+/uwhBoIaj/ab+EyJnxpLVEzBYoFgUBcyEiUUeb2w7VmwabyuVtIbtp3bYukmRw5BaBhCYfs1QyEezYCihjBJYRs5VQFzPjC+hthiHGVRZ4xQNMOOotHgKKWOMqzGWVpFKgqcPKl3Dl12mZ4vNJWurZISuPxyvetozx5dyFu7dnZjEYynsBBycmB0VM8rmgBZknFZcmkbbhv/gGQAObE2oql/tSgQTIFvPNYYsQc6nRM9o/z5zRa+9NBeLvvBS5z3jae56/FGlNDEy2cax7tj8okK01coumxxNKfo1ZiQa8F4jsX8v4VQJEgFYoNNd6dRTlFI1XgrLHAVOizUpvH3pyB7WRa2dwqGNA53ntsbO1XYdlx3JTAbZdbME518gvRhRaU7cnuf6Coax3MHdaFIluBtDUIAFsSfdbXRCT2xE32ygSf2dXCqzwPAhQ2F46xnBYJE4rAY+ch8vagdQuKnXRMIFWYznDqVuEG99RIcfg2K8njMF+2uucrWrt+I6SbCXIVP0d14opPLVTCc6WbTYhqhpnP2HUWKqlCcUzSjdSMMDcGhQ3r30PvfDxddNH1rv6IiuPRSuOUWcLtnNx7BePLy9G66SXKKci25Z2YUAcxWRJwmQigSpD0vHu7mqcZOAIqcFl78p4v5+YfW8PG31bG62o3JMF75H/AE+eXLJ/jeU4eTMdyEc7wnpsMkjWdWLS7LJcesz+oQlh5n59i4jiJR2BYkn/NihKJdaSQUHWgfYtivn6itq83Lqk5UQeawLCYHZF8a2M8193lo7tPtU9dU52E1zSwUWCBIBrFB3nvSYH9LFKd6PZHz09XVebjt6WNBK0gfxgtF2TOpUNM07n7hWOT+nReJ8HlBcrm1zoIT/RrqoT4TzYHTrqFcLl0o8nrnfjADbfDsb8GmotjL+LtPn3xskxQusegTkwjECkXVeBR/zBNogKTnxMQQQqXDMEJNuFk25LCiWabfRRxUgxhkAy7rLMTd0VFobYULLtBForpZ5pOdxRpNMAtkGaqrJxWKcsxnqd/JiY1TmZL13KkYtbe6unrCv8+E2OcSCGZCQFH5t7/uj9z/8jsXMa8gh3kFOVyxVA+p8wVD7G0dZEdTH2829fPC4W5CqsbPXjzGmnl5vH1JydmePiMY11GUxh0mBlliWYWL10/00TrgpXvYT5Ezu/OnJmJcR1EaC4OCzKEyz0Z+jpm+0QC7mwfSxjd+R5OwnROkP8vKcyO397YO8oEkjmUqxHYMi3wiQboR21G0p2UgaeNINV44HLWdu0TYzgnmiGXlLmwmA95giNezKKfoxcPdHAjbs6+sdLFRHDsFSSbXJPNhQyc/ClWgIHF3l4VvVcZkFeXmQlOTnlM0b97cDUTxwkv3QXMTLF7Kq4EC+lV9osIWSzd2OQSqD5TwMUrOBYOLoWAX5rFMGFXR82EM4zs6Oowe8gc0bAH9/ky7iTxBD3aTHZdlhkJRMAgnTug2c29/OxjEBKuUpawMQqFzLmIz2pAlmZAWwiDF/C/lxFoZT0koqq2tBfTWO0VRzvj7TDj9uQSCmXDf1hORjpk18/J473kVZyxjNRk4vyY/Uuj71cvH+ebjBwD4/B938dinL2BeQeZ2XozN4LOaZMpyE9uyGG9WVrl5PWwlsKdlgEsXZ7bINxPGMoqMskR1vn2SpQWCuUeSJFZVuXnuYBf9niCn+jxp8Z0rhCJBJrA0tqOobSiJI5kaW4/1RG5vqhfFLkF6UV/siBSqRfd7lOcPxghFaZRTKEgvzEaZ1fPcbD3aS/ugj5Z+L1VZcC3009huoovrs0IcE6Q4/gE+HDrCPXI5I6rEn/pMfKrYT7k5bNNmNut5Op2dcycUqQocfwxeexkKysFo4vGR0sjD77J16DeCLehdQ4C5ClVTGVa8mOSxfKIgSCaQx3fCthhHqW2PtZ2b2QRdT9BDrbsWk2EGQoCmwdGjsGgRbNkiRKJUp7BQ3/YDAf33BNhMNswGM4FQAJsxxu7QkNhO7Cn1lGmaFvk5299n8iMQzIbOIR///ewRACQJ/u3qpVM6MfrIBbVcuVw/SAz7FO787Vv4gudWdtOVgKJG/IrrCh3IcnqfOK6Mmam5W1yAn4GqapEOsnkFdkwG0TYsSA1i9910sJ/TNI3tJ/R8IqfFyOKy3EnWEAhSE4fFGMknPNA+RDCF8xk1TWNbuKPIbjaM684QCNIBvftdP1609HvpHfFPskbm4wuGIvt1Sa6FxWUzm3UtEEyFdTXRCQbZkFP05sm+yPusK8rh8gx3ShGkCZ5mXMP7uS1Pn6AU1CR+dnpWkdUKx4+DzzfBE8wSTYPul2HH32HAACWFBDWJv3v1/cMuKVxs6daXDcS4ZJmq8asKQVWJdhRpQTDaOb103mIcoaZrdvlEGhohLUSBfYYTo5qaoLgYrrgCclJ/EmbWU1gITuc57edsRhumsFA0DmlKPT5xY0qvdt99903r7wJBIviPJw4wGtAFnpvWVY/z4T8XkiTxnetWcKB9mBM9ozS2D/H1v+7n29etmMvhJoVTfR5Cqn4Aq8uAvJpY7/fdaVBsTjStA178il4ETGebQUHmsbIqdt8d5JpVZ3Z/phJNvR56wgW+1fPyMKS5yC7IbpZVuDjeM0pAUTnSOcKS8tQUPo91j9A9rO9362rzxWQHQVqyotLNjiZ9osGe1sGs76DZdqwncm56ycJi0e0gmFPG5xT1cd2ayiSOZu6JzSa646L5aT8pVJBBBPr4sOFV7pUvx6PK/G+fiX8o8VNqCosrhYWwfz/09cGKFdDQoIse8ThG9L0JTc/AoWHIywODzFZfAQOa3pVxqbUbm6zqIlCwXV9HsoKxEF/AQyAUwjHWzaGFwHhmHa3FNMLqjuj9UMH0hSK/4sditOC2uqe9Lt3deu7N5Zfrn5sg9bHZ9P/VyZNQMLE4aJANOM0Oejw9Ez6eKKYkFN16663T+rtAMNfsaOrjkV1tALjtJv7x8oXTWt9pNXH3zat5z0+24guq/O+OZtbMy+N9a6vmYrhJI1PyicaozLNRkGOmdzTAnpb0yTpJFOPyiTLg/y3IHFZVuSO3dzX3J28gU2RHzCzU2KKDQJCOLKvI5a+79XOmfW2DKSsUbRP5RIIMIHZS094WIRQ9tqc9cnuLyCcSzDHnVbsxGSSCIY3tTZndUXSoY5hnDui2jmUuK+9J8UlYgixDNpEfaOFD9iZ+PlJHQJP4eZeZr1WEO21zc2HBAj2n6O9/h61bob4eliyBujqwzDCLeugQdDwDJ0agLwD1RQA87o3azl1lDR+Xgu1A2FnIXAWShDcUQNFCGCWZiCWdfGZ8Q2xHkWo2ouZMP+LBq3jJMeXgNE9TZBoZgZ4euPJK/TMUpA/V1XDw4DkXcVvdtA23n3OZuUZM1ROkHSFV42uP7o/c/8LlC8nLmb5n46LSXO56z/LI/a88so/GNPDvnw5j+UQA8zOgo0iSpMgFeL8nSEu/N8kjSi0y7f8tyBzcdjM1BbpP/L621La/AsYVF0Q+kSDdie24TuVu3G1HY4WiwiSORCCYObGWiXtaBpI2jlTAFwzx1P5OAJxWIxctLEryiASZjtVkiNgdn+gZpWtoDmytUoSfvxjtJvrohXWYjaK0J0ghJAmsJXxM3oZV0sWYB3vNdAVjJvkajVBeDkuXgtutdxj97//CvffCK6/oGUbTwdMCbX+DUT8cHAS3A2SZgCbxpE+3ncuRFC62hrs1As3RdU3VAPjCll+SJIEa0i2/TssnGpIDSL4A+eE5uqF854w6oXyKjwJ7AbI8jX03ENA7Utatg/PPn/ZrCpJMUZG+rahnr4XYTXYiImWSmNXRpK6ujrq6On784x/HazwCwaQ8uP0Uje26oLOkLJeb1lXP+LmuW1PJjeH1/YrKP/zuTYZ8wbiMMxU4noEdJivSLOskkYzrKCrOjP+3IHMY6yoKKCoH28/uzZsK7AgLRWajPG52uECQjiyvcGEy6Bewj+1pZ9SvJHlEZ6KqGq8e14Uil83EEpELJkhTagrsOK26aceeLM/TfOFQFyPh75srlpZiMYqgbcHcM85+LkO7ipr7PDy6O+qu8oHzM8sVRZAhyCYKbQ5uNjcC4NckftV9lgneubm6/dz8+eDxwJNPwv33w1/+oncdTYa/F1qfgMAgnNKgZwAK9Wu4rf4ChjQTAG+3dmGVVNBUCI4JRUYw6R1Ho0oACIs+WlAXigzju5tajCPUdMbkE83Adg5A1VRclmlcZ6oqHD0KixbBli1gEMfUtKOgAOx2fRs/C7pQJKGSvIm1sxKKWlpaOHnyJKtWrYrTcASCc9M3GuB7Tx6K3P+3a5bOOjvia+9eEgmeber18E9/2o2mJVfBjRfHe6IdJrWFmdFhEmthle0zNU9nnDBYKIQiQWqxMtZ+LoX33a4hHyd79ZO3VZVurCZxEi5Ib5xWUyQXbNAb5E9vNE+yRuJpbB9i0KtP1NlYVyByFgRpS2z3e9ewn47BzO1omIz/2x21Tnn3yvIkjkSQTcQKRbFWwpnEr14+Hskhvm1TDTmWxAadCwTnRNOIdEQYrHw8pwkL+qSB3/Sa6VXOcY5nMuldRkuWgMsFu3fDn/8MJ06cfZ3giN5J5GkGymHvcXA7QZZRNbh3tCay6FW28HFJ6QJN7x7CXAGSfr03FPRglsP7kxoEo00Xi2JoNo5QE9PsFMqfvlAU0kLIkjw927mmJigrgyuu0MUGQfqRn69v18NnnzRrM9owG00ElEACBzaeWQlFpaW66mqz2eIyGIFgMr731KFIIeG951XExRLIajJw9wfXkBue/ffk/k5+9fI5DkRpxFiHSWmuNWNOIGNn9+/O8pmapzNmPVfosOCym5I8GoFgPLEibyrbX42znavNS+JIBIL48bEL6yK379l6AiXF7B9fjc0nqhf5RIL0Jrb7fXcKT4yYS0b8Cs8c0Ctp+TlmNovcMUGCWDMvj7G5Bq9noFA05AvypzdbALCbDdy6sSa5AxIIYjm5DRp/DPaeyJ+KrSZutOpWiV71HF1Fp+NywcKF0NcHDz0Ee/aERagYPG3Q+hgMHQBnAxxqgd6hSDfRz0Zqedmv2xkXyn4utITPNyewnVNVlZGgD7McniSoKWA8c7J1i2l8R5EyA6HIp/iwGq04zFOc3NvZqVv1XX65bl8mSE9kWc8pOpdQZLJhls0E1OQ5Xc1KKFq/fj0A+/fvn2RJgWD27Gsd5PfbTwGQYzbw5XcuittzV+Xb+eENqyL3v/33g2xP8xPLvtEAAx79y2V+cWZ0EwEUOCxUuHVxel/rYGQ2VbYz6A3SPayHQ9aJfCJBCrK4LDdif/Xmyf6U7dyM/e4X+USCTGFhqZOLFugXls19Xp7cP03f9zlm67FoQWGTKCgL0pwVMblge7N0UtMzjZ34FV2QvnJ5KUaDyE8RJAan1cSSct0t5FDnMH2jyZuVPRc8urMVT0DPfLl2dcWMspoFgjmhvwnuuxJGT4GjE9TovneHqw0z+nb76x4T/efqKopFlnU7Ok2DRx+FrVshFIKQD7q2QtPvYLARHA0wEoC9JyA/F2SJbf58vje8AAAJjR+494Rt5zQInhp7ATDpXfc+NUhAVTDJMROsZesZQ2oxjkaEIs0go7qn393jU3zkmHKwGs98/jMYGoKBAd1urr5+2q8lSDFKS/Vt+CyYDCZyzDkEQ2naUXTnnXeiaRo//OEPCQYzJ9dFkHqoqsZXH90XmUDwmUsbKM6dwpfqNLh0cQn/cPF8AEKqxqcefCtSeE9HYm3I6jLMhmysM8ETCHG0a+TcC2cJmZhHJcgsrCZDZN890TPKW6f6kzugszAmFMmSPitVIMgUPv62aFfRL146ljJibTCkRva7IqdFHMMEac+K2A7aLO0o+ms4PwXg6pUVSRyJIBvZNF/vINA0+PObqWe3OlM0TeN3r5+K3L9p3bwkjkYgOI28Glhxg35bDoFvb+ShUoOf99v1TrhRVea+rmlaDFdUQF4ePPUUPHI/HP4NtP8dZAu4Fus5QgdPQd8w5OfSGbLwmf6VqOG8of/nPMrbrOFuolA/qOGIBmMJyLrYOqr4CaghLLIRtBAgn5FPFESlXx2lLDyvMOTO0cWsaeJT/BTYC5CkST6HUAiam2HDBlizZtqvI0hBCgt1i8XA2YUgl8WFP5S8WvSshKItW7bw5S9/md27d/Oud72L5ubMOQgLUouHd7by1qkBQO+WuH1z7Zy8zuffvoCNdfpM1q5hPz969vCcvE4iON4dzSfKtA6TcfZzKWxhlUiOxfy/52fY/1uQOXzg/OrI7fu3nUziSCZm0BvkUKfeCr64LBenVVg4CjKHTfMLWFymz7Le3TLIjqbUEGv3tAxEZkdvmj+Fi2aBIMUpd1kpdOiFp72tgykjyiaKAU+Alw53A1DmsrJWTLoQJJgbzq+K3H5g28mUs1udKTubBzjYoZ+nnlftjnROCQQpw5Z/jWb6+A5AKFqjuNNxAhP6vnhfj5XB4Nm7KibElQPOYXjqt/B/T4FWDtawDdvgSLibyIkiyXy6fyU9qi7yXGjp4dOOY9HnCUTFVszRa9PhoBdVVTHIMqgKyCZdiIqh3ThKZbcWKaSHCqZvOwegoeKyuiZfcGAACgpg8+YZCVKCFKSwEByOc9rPOcyOpJ47ziq05N///d+xWCwsX76cp59+mrq6OjZv3syKFSvIy8vDYDh3APRXv/rV2by8IEvwBUN8++8HI/e/9u6lmI1z8yVpNMj8943ncfF3n2c0EOKPb7To3UvO+HYvJYJjGdxhcrr3+/tjLgaylXH/7+LM+n8LMoerVpRx1xMH6BsN8Le97XRdtTju3aGz4c2TfZHOVWE7J8g0JEni42+r5XN/2A3AL146Pi70O1lsOxqTTyRs5wQZgCRJLK9w8fyhbgY8QZr7vFQXZE/w9N/3daCEraHftaIMWRbiryCxzC9ycMnCIp4/1E3rgG63etWKsmQPa9b87rXYbqLqcywpECQJdzWUbIaOFwEVvLvAsRmACqOP6+2t/N5TxbBm5P6WQf5fiQ8MNjDadFFmoslCmgbedj2HSOmB2io4NQRP7oRLVkFxHhw4Bf0j0FDBd4ca2B7Qz2/LZC8/cu/GEPu0wZgGB3O0jtQfGNVFIgAtALJNF4tiaDGNUHMqWsAPzSCfKKgGMcpGcsxTmNzb3w/LloFzZoKUIAWx26G4GE6d0kXAiRYx6eeMGhoSiT+HmpVQ9PWvfz0y60+SJEKhEC+//DIvv/zylNYXQpFgKjyyszViAXfZ4pKIx/5cUeS08MEN8/jFS8cJKCr3vHKCL79z8Zy+5lxwLIM7ipZXupAk/ZxhT5Z6v59OrPVcfYYJg4LMwWoycOO6Kn7y/DEUVePB7af47GULkj2sCNtPRDssUqGALhDEm3etKOc7fztEx5CPZw92cqx7JOmTSbYdixWKCpM4EoEgfqyodPP8Ib2rZnfLQFYJRbG2c+9eWZ7EkQiymY9cUBfZB+955XjaC0WDniCP7dH3LafVyLtWiH1LkKKUb4G2V3T7ucAxUJaAUe8s/QfHcf7oqSCEzK+Gy7hQe5LVpn5dJDJYweQCc64uHhnsIBlg+BiMNukikr0CJBnmO+FkBzyxHdY0wL4TUJDLk74Sfj6qWy0bUflJ/i7yDTExKaFh3XoOwFAIsn5sDqkqvf4RbIZw5peqgDkHTivStxhHmd8ZFYqUGQhFPsWHzWjDaZpkXU3T7clq58ZNSZBEqqvh0KGzPmwz2TDKRoJqELOc+By6WbdlaJoW+Tn9/mQ/AsFkaJrGfVubIvc/vSUx4W0fvaAWczh09bevnmTQk34ZXMd7dOHAapIpd9mSPJr44rAYI2LIgfYhfNNtW85AxoRBs1Gm3J1Z/29BZvHB9fMYm1z8u9dPEVBSxw5kR1Nf5LboKBJkIiaDzIcvqAH06897XjmR1PH4giHeDOeVVeXbqMrPnmK6ILOJtUne25o9k5q6hny8elwXf+cV2FleMQVrHYFgDthcX8DCEr0Q+9apAXamaDbmVHloZwv+8DnzdasrsZnP7d4jECQNox1GiqP3vW9GblYZvVxr0wXPYc3C+4bfxU8C6whJJlA8MHoS+ndBz6vQ9SJ0vQQjR8HsAmuJLhIBGGSoLYNRL7yyDwZHOJlbwj8OLI+81r/mHmK1+bTj71m6iUYUH14lgNUw1kGkgfFM14sW4wg1YaFIA0L5059s5VN8OC1OzMZJBICREd2irELk/GUcReHmB3XiOojNaMNitBBQzp5jNJfMSihSVXVWPwLBZLx6vDeSF7G62s3KmHDYuaQ418r1aysBGA2E+PWrTQl53XgRDKmc6vUAUFOQk5GWD2P2c4qqcaB9KLmDSTLBkMrJXl0oqivMwZCB/29B5lDutnH5klIAuof9/H1/R5JHpOMLhtgTDh2vK8yhyGk59woCQZrygXXVOCy6qcBf3myhZyR5YalvneyPiMWb6kQ3kSBzGGeTnEV5mo/vbY9YuF69slxkjgmShiRJkYkRAPfGTD5NNzRN48HXY2zn1gvbOUGK4ykEOexqE2yDYHvkoX9xHWKNSRduQ8h8d2QxNw9eTIexDGylYKvQf8z5uuhkK9c7jE5HkqCqGHKs+MpLubP/PIY1Xei5ytrObTkT5OEGJhaKhoJeAqqCWTaiS0ASyOOFIg2NNsMw1XqjImquHUzTN+kKhAIU2KZgtdzfDyUleqaNILMoLIScHPB4JnzYYrRgM9oIhNJQKBII5prYbqLbNie25fKOt82PzHq/d+sJPAEloa8/G071eSLe4JmaV7OqKjpDMdvt55r7PARD4f+3sJ0TpAG3bqqJ3P71tqakjSOWXc0Dkf1IdBMJMplcq4kb1+kXx35F5TevTnAhnSDG2c7Vi3wiQeZQ5LRQ7tKLTPtaB1HV7HDT+D9hOydIIa5ZVUF+jj5r/4m97bQNeJM8opmxo6mfI126W8j5NXksKBF5JYJURwbbedG7njcZm0WQJwf5Q+F2PuM4ioT+t1cDBbyzezPP+P5/9u47PK76yv/4507XSDPq1bYs2ca9G4xNMYaAbUIvCWmQEEKANdkFQhJINiQkv4RsspuE3VBCCZAQEkogdIhpptkUGzdcwEWWmyyr1+nz++PKIxmrWJY0I43eLx4/ntHccox0pdH33HNOhzETFruZIDJ6WLbOSNNPgzO1MeSVJI2xNem/MjYcPu4o4pNClW3H9kqW9vWkxqD5vcEwDCkSlCw2yXroTYN1Fr/Sa0NytC0LHs18omjbv9fjPIJ9m5qk8eM7n9uEoS0zU/J6pcbGLjdJd6YrECFRBByivLpFr2zaL0kq8Lp05tSCuJ6/ONutc9t+waltCerv7+/qYY/BY3uH+URjc5JrPtFBw/VOzc50nEc1NsnmUSE5zRuTpfH5ZlLzw5212jAI2vJ8sKND2znmEyHJXX5iqWxtd8P8ZeVOtQYS08L13W1Vscfzx5AoQnKZ1tZ+rjkQjrWETma7alq0urxOkjSxwMNiNhLOZbfqa23VN+FIVH9O4I0RffHIe+1xU02EIcNRKlnN2UQK10iB9nbHNiOqG7xb9Uj2Byqw+CRJtRGHvlUzRz+tnyRf9MiXqh9vGaG/t5g3QLmMsO7KXKM0Syfva4O7pbZEjRyjDknAVPka5bS0tZ2LBCXDIX1mNsweW7NKO8wnCmf3/gbdQDggu9WuNEcP+/r9ksNB27lkZbWac4q6SRR5nB6FI4n5/axPiaLS0lKNHTtWW7duPeJ9ysvLNWbMGI0dO7Yvp8Yw8NCKsljrgkvnj5bdGv+85jUL22ci3fPm9kE1S6M72w+0/zI6JkkrTCYWemJzpNa2tYsarrZ1+HwnawUZkothGLpsfknseSIrGg56v8N8orlUFCHJFWWk6Oy2wd41zQH9Y/XuuMfQ6AtqbVtF8Li8NOV5D+8FDwxlHW9qOphASWbPrWtvLUQ1EQaLr80fLbvVXBD+2/vlQ6pLiGT+jH5hg9mmOcNt15lTCxMcEdADi7Wte5shuee0f7x1jRQ9dOF7vrNGL+a9ozNc+2Mfe7B5tM4/ME+fBru+ATYQNbQn5NLrvhz9uH5y7OO/TP9YE+xd3JjRse2cvb3tXGs4oMaQT66DM4MiQcmWqs8ul++1NcfmE0lS6Cgqinwhn1w2V8+JotpaKStLKuR6T1qFhVK460RQij1xc8f7tPK+c+dOlZWVKRA48nKoYDCosrIylZWV9eXUSHJN/pAe+8D8Ru60WfTluYm5c2ZCgUenT8qXJFU0+PTUR/FfSDkahyQOkjRR5LRZNanQ/OG8vapZDb5ggiNKnO3D4PON5HPBrBHyuMy+zv9cs0e1zYkprZakUDii1TvNXtn5XqdGZSXujRkQL986eUzs8f1v71A4zq2xPiiriZ3zhLFUEyH5zO1QnfrShsExj28gPdOh7dzBRDSQaHkeVyxxWd8a1D9WDY3f5w/6x6rdsZtVL549Ui67NcERAT1wFZjVOKEWyV4k2dp+HkSaJP+WwzbPtAR1T+ZH+nn6x3LIXDjfHPLqnKoT9LuGcfp941jdXDdF36yerc9XnqBjK07V+H2LdWLlQl1ec6x8UfOa+Iq7XBe69x52fElSNGTOSpIkwyXZ2uf+NAV98oWDSjlYUaSIORvpM/bamzW6sv350bSe84V8ynRlymbpYbZRfb00bpxZVYTklJMj2WxSF/kUt90tq8WqUCT+NzfQeg6D0pOrd6vRb14Q53foLZwI/3Zqe/Xb3cu3x30h5Wh0bD1XmsStyA7eqRmNShuG8Zyijq3nSpO01SCST6rTpi/MaZ+T8tiHiWvvuXFfg5rbWm8dV5LF8G0MC1NHpMcSNDuqmmPtfuPl3a0d5hORKEISmlOcqcK2OUVvfnJANQm8IWKgba1s1KZ9DZKkGaMyNDqb96MYPK44qX3W8QPvlA2ZmWHRaFR/e7889vzLtJ3DUODMkhyZUtD8mXBoVdE6qZO5K4YhXZq6S8/krtAxNrMdly9q1e1N4/T7xmP0t5ZRes2fp40hr6oizsP2n2av1y3pm7uOKbhXaktCmW3n2pfCG4OtikQislosaiuFkiyHn2OPtUmlFeb3jnCKQ1H34dv0JBQJKTMls/uNIhFzgWv06F4fH0NITo7k8XTZfi7FliKn1alAOP7vHeOeKKqvNxdz3e7DM7SAJEUiUT34Tlns+TdOLElYLJI0uzgz1jd/R1WzXtywr4c9Em97lZk4yPc6lebs4W6FIWzGqIzY47XDNFEUjUa1tW24aWG6S6lJ/PlG8rl0fvsb4L+s3JmwRPz7HeYTzWU+EYaRKxe0VxXd++b2uJ03EonqrU/N+USGIc1jPhGSkMVixOadhiJRvbB+8P8OcbSeXduh7RzVRBhkphSla94Y8/3d9qpmvfFJZQ97DA4rtlfHfq+fPyabzhEYOlx5kiJSJCTZsiRH2/vNaEDyre9yt4n2Jj2Ts0JfdZd3+rpNERVZWzXTXqclrgp9PXWnbk3fqMey35PL6GZMRBdt5ySpNtDcliSSGa9hlayHJoEiiirQ0iKPr+35UVQTRWTG53H0sG99vZSeLhXRwjWpud1SXp7U1HmrRJfNJafNKX/YH+fAEpAoevjhhyVJo8mOogvLPz0Qe0M0b0yWJhV6ExyRtPTU9llFd76+TdHo4L0LqbY5ELtjMdnfTM5oGxIsSWt31SUukASqaQ6ovtVsu5fsn28kn9KcVJ0yPleStLu2Va9vTswv7h90mE90HPOJMIwsHJ+rY9pm2324s1ar2lowDrT7396hLfvNO+imjUhXhpvWGkhO585sX+h5es2eBEYycKLRqJ5taztnGNLZ01ncwuDzzRPbq4r+9HZZ4gLphb++175Y/hWqiTCUODIke3p7VVHKTMWWn32bpHBzFztKKZaIfpGxUU/nvKvb0jfovqxVei7nXb2f/5o+KfyX3s1frn/mrtTdWWt0a/omfT21XCmWbpJE0YgUPNhy0ibZ229mCEciqvY3KcV6cD5RSLLYD6soqrK2atT+9nkyoezeJ4oCoYCcNmfP84lqaqSRIyVv4tdBMcCKi6WWlk5fMgxDXqc3IRVFvbr1/LTTTuv045dffrlSU7svL/f7/dq+fbsqKytlGIYWLVrUm1NjGHmgQzXR5R3e0CXSieOyNX1kutbtrtfGfQ1645MDOnVCXqLD6tT2qvaM9JgkbjsnSWNy05TqsKo5ENa63XWJDichOradG5vkn28kp6+fMFrLPzkgSXpoRZlOn5wf1/NHo1F9WGYujntdNk3I7/0bf2CoMgxDVy4Yo+8/sU6SdN9b2zVn9Jwe9uqbNbvq9F8vtbcHuXHRhAE9H5BIkwu9GpeXpq2VTfqgrFa7a1s0MjO5Omt8vLchdpPf3JIsFbS12wMGk89NytfobLd2Vrfo7a1V2lzRoIkFg3chtqrJr399bM42y051aPGUggRHBPSCxSa5i6W6tZKyJGua5Joo+TZKikita6S0E7s9xAxHg2Y4GvoeS6hSirZVZThGmBVDbZpCPrWGAvLa2+bTRoOSzXvINpK019asMRXtN4uHs3v/vaM11CqXLUWp9h7WbPx+cz4Rkl+uecOsolHzTpvPSHema2dkp3rf5LBvepUoeuONN2QYxiHVFNFoVB988EGvTjpmzBjdfPPNvdoHw8PWyia92bZgOCorRadPiu+CYVcMw9C/LRynqx9eJUm66/VtgzZR1DFxMCYnuStMrBZD00ama+X2Gu2t96my0ac8z/D65XT7gfbE4Ni85P58IzktHJ+n4iy3ymta9NanVdpa2aRxcfxa3nagWdVtVZjHlWTJYmE+EYaX82YW6Tcvb9GBRr9e/rhCO6ubB2y+SIMvqO/8bbVCbW0mrz5lrBa0VRUCycgwDJ0/s0j//a9PJJkt2q5ZOLaHvYaWg9VEknTODKqJMDhZLYYuP6FEP312oyTpT2/v0K8vnpHgqLr2+Ie7FQybPyu/cOwoOWyMF8cQk1IgNW6RQi2SzS25pkn+rWb7ucA2KTRZsvUwr6evIgGptUOru8+0nWsItioQCclhaVsaj4Yk2+HvgffYmjWlov15OKf3Nxb6wn4VpBXIYunmWm5uNluS0XZueMjOllJTzc972uHrHykHE5hx1qufNgsWLNCCBQt0yimn6JRTTpFkvvk99thjYx/r7M/ChQu1ZMkSXX755br33nu1du1a5ebySyEO99C7ZbHHX59fIusgWrBbNDk/tnj5flnNITMtBpPtHRNFw6DCZMbIjNjjdbuG35yibR0TRbSewxBksRi6rMOsoodX7ozr+Q9pO8d8IgxDTptV3zihRJIUiUr3vbVjQM4TjUZ18z/Wa1dNqyRpdnGGvrto/ICcCxhMzp0xIvY42drPRSJRPbfOnE9ktRj6/DTmE2HwuvjYUfK0zXP955q9qmqK/+yHIxGJRPW399vbzn157qhutgYGKUe65Cpobz9ncZrJooNaPjQrKQZK6IDU8JwUOjhDzybZRx6ySWPQfE9qdKzmsB5+4/Fea5PG7jNjDblsiqT2/ubkcCSkzJQeEmO1tWaVSd7gvCkd/SwrS/J4pMbGTl92292yGBaFo920VhwAva4o6uhgJvTBBx/U5MmT+y0oDE/1rUH9Y7XZO9TtsOoLxw6uN0QWi6FrThmr7z6+VpJ05xtbNbd0boKjOtz2YZY4mN4xUbS7Lu5tqxJt2zBLDCI5fWHOKP3Pvz5RazCsJ1bt1o2LJyjN2au3KEclGo3q5Y/bbw9jPhGGq68eX6w7Xt+qlkBYf1m5U9NHpvf7+7BH3i/X8+vNX9a9Lpv+98uzZLdyhzSSX3G2W7OKM/RReZ02VzRqS0WjJhQkR5vT1eW12lNnLrSdNC5HWanMG8Pglea06UtzR+net3YoEIroryvL9R+nH5PosA7z9tYqldeYcytOPiZnwKp8gQHnHim1lLfN/rGZ7ef8m6VIs5nAaXpVSj1RsvRj5UQ0Kvk+llo/ktSWiDIcUurJkuXQn1FVvkY5LXbzSSRstpyzHN7oq6W1Sd7Wts2yvZ22CetOOBqWxbAo1dHDtdzYKM2bJ3VXdYTkYbWac4pWrZIKD7/RJsWWIofVoZAvGNew+vTVd9lll+myyy5TZuYAlwtiWHjsg11qCZgD4i6aPVLpKfYER3S4c2cWaUSG+UPsjS0H9PHewVfBcrDCxGmzxGJNZjNGpccer909+D4fA+3g59vtsKrAO7za7iF5pLvtOn+Wecd1kz+kJ1fv7mGP/vGnd8r0xhaz3WlWqkPTRqT3sAeQnDLcDi09tb0f+vf/sU7//Kj/Kh827WvQrW3tfiTp1xfPSLo5LUB3zuvQki2Zqoo6tp07l7ZzGAIum1+ig01L/rJyp/yhcPc7JMAj77VXE31lbnECIwH6yJUr2dPbq4oMq+Q+rv314F6p/lnz7/4QaTWTT62rFUsS2XIl79nmfKIOWsMBNYZ8ctnakkcRv5lQ+kxFUUgReQ60xp4fzXwif8gvl80lj72bm0QCATNxMGJE19sg+RQWSuHOfw6l2FLktDkVCAfiGlKfEkUPPvigHnjgARV2kvkCeiMcieqhFWWx5984sSRhsXTHbrXoqlPGxJ7f+ca2BEZzuFA4Erv7qDQndVjM2hiRkaLstrsX1+6uO2SGWrLzBcPa1fb5HpubdmjJNDDEdGw/99C7ZQN+LX9QVqPbXtgUe/6rC6fR/x3D2r8tHKvL295/RaPSDY+t0fPr9nW/0xFoCYR07SOrFQiZbRO+Pn+0lkxlKDeGl7OmF8Vaaj+9Zm9SvF8NhSOxKkGHzaJFU4ZXVT+GplFZ7tjPoKomv55d2/efc/1pf4NPyzbtlyTlepzDrlsGkozFLrmLpXBL+8ccxVLa5ySjLSET9UmNr7S1outD4ja4T6p/7tCkk2ua5FksWQ/vtNMU9MkXDiolVlHkkxyZknFoV4v9thaV7m9v/RXO6X2iyBfyyW13dz9zpq5OysxkPtFwk5lpVpB1kiyyWCzyODwKRYZQRRHQX17ZtF+7a80s/Snjcwd1y7QvHjtKOWlmYuKF9fsOafWWaLtqW2NDLwfz/8P+ZBiGpo80qwDqWoKx2QfDwc7qFrXNA9dY2s5hiJtU6NXcthlB2w40691t1QN2rspGn5b+dbVCbRfQNQvHatEUFq4xvBmGoVvOnqyvzTPvXo5EpX//+0eHtGc8Grc8/XGsTerkQq9u/vykPscKDDW5HqdOHJcjSdpT16rV5bUJjqjvVm6vUVWTeZfraRPy5HENvm4QQGeuOKk09vj+t3cMqsTtYx/sUrjt/eklx46iRSuGvpQCyZoihTomi0ZI6edI9g5JEd9GqeElKdzQu+NHI1LLR1LjMinathZkuCTP6ZJ7lmR0fg01BlsViURktVjaElQWyXF4EmiPrVljOuSTQ0dRUeQL+5Tjzun+xt7aWmnsWMlFl5hhJT1dSkmRWjtfx0x3ZSgYHkKJovXr12vMmDE65phjtGdPzyX0e/bs0bhx4zR27Fh98sknfTk1kswD77QPTr58kFYTHeSyW3XFSWZVUTQq/fe/tgyaN5cdk1bDaV7NjFEZscdrdtclLI54G27zqJD8vj6/JPb4oXfLBuQcwXBE1z7ykSobzQHGJ4zN1nfPGD8g5wKGGsMw9LNzp+qStvlE4UhU1z6yWq9t3n9Ux3vqo916YlX7/Mk/fGWWXHZrv8ULDCWHtp/rpzY7CfTkR+1tYs+h7RyGkNnFmZrRdqPhpn0N+t2yT+QLJr4FXTAc0d8/2CXJHIHypbmDa2YzcCTK6sq0u6FDG3FHuuTKl4KfGRNgSTEri9zHKrY0Ha42q4L828zFtu5EfFJwv9T4L8m3vv3jtsLDk1CdqPE3m0kiSQr7zGSW7fDWcHutTRpTYcYScFkVTT18hlFPItGIvM5uEkyRiPmnpKTXx8YQ5/V2myhy21Pi3jmoT4mihx9+WGVlZRo3bpxGHEEfxREjRmj8+PEqKyvTww8/3JdTI4ls2tegldtrJJnJjQXH5CY4op59bV6xvC6zJPWF9RWDpgXdtuGaKBqZEXu8blddwuKIt46f77F5JIow9C2akh+btbVs0379eUX/t6D79Uub9f4O82dOgdel//3yLNm4WxOIsVgM3XbhNF3YNjcsGI7q6r+s1vJPDvTqONsPNOlHT22IPf/FBVM1hpsaMIwtmpIvZ1uL0+fX7VMwHOlhj8HrQKNfz7W17PK6bDptYl6CIwKOnGEYuuLk9nby//vaVi35/Zt6fUtlAqOS/uvFzdpTZy4WLhyfyyw/DEmN/kbtadijpkBz+wdTR0qKSpHQoRsbhuSaLHnPlCwHEykhqfkdqfltKRKQws1mOznfJql5pdTwslT7mFT3mNT4shQ6eN0aUsoss5LI0v2s7nAkoppAk1KsHeYTObPMGUqf0dTaKG/bGn4wx2PG3AuhSEg2i01pjm7eAzc2mgkD2s4NP3a7lJ3dZaIoxZ4i+8Gv0zjp08rI8uXLZRiGzj333CPe57zzzlM0GtWrr77al1MjiRxSTXRCyZCYq+Nx2fVfF02PPf/Ny1v0wvrE9zfefqD9h/GYnOGzGHOw9Zwkrdtd382WyWVbx8/3MEoMInnZrRZ986QSSeZNZLc8/bFu+sf6fhs0/ML6fbr3rR1t5zJ0x1dnKyet93eFAcnOYjH0my/MiFUJBMIRffvPH+rdrVVHtL8vGNa1j3ykloB57V48Z6QumDVywOIFhgKPy67TJ5nzRqqbA3rnCK+nweiR98oVaEt0fWlusVIcVApiaDlneqGuWTg2NjusrLpFlz/wga7884exGbDx9Py6fbrv7fb3qNedTrU7hq4Gf4PK68vbP+DMk+zpUrCLtnK2bCn9LMkxtv1jgR1S3aNS/T/aZhh9IPk/kUL7zblGHVlSJc8SKWXaESVymkI+tYYCclntZvLKsEr2zit+nFXtN+caWRk9HvuzfCGfXFZX94mimhqpsNCcV4PhJy+v64oim1t2a3xb+/YpUXSwfdz06dN72LLd1KlTJUlbtmzpy6mRJKqb/PpnW+sFj8umC2cPnUWEM6cV6nuLJ8Se3/DYGq1NcDXL9mGaOMhOc2pkpnnXyPo99QoN4Ts0e+NgRZFhSCXZw+fzjeR2xUljdNWC9rs8H/1wl758z0pVNvi62atn2w406XuPr409/8+zJmvOaN6MA12xWgz99oszdGbb0G9/KKIrHvowVpH3Wb5gWFsrG/XKxv363hPrtHGfuRgwNjdVPztvStziBgazc2cO/fZzgVBED7+3U5JkMaTL5o9OcERA7xmGoR8smajn//0kzS3Jin182cb9Ov23y/W/r34at3Z0Wysb9b0n2t+j3nLOlENaqwNDTSQSUVndjvaqIotNSh0thVu6biln2KW0E6XUk83HkqSutk2RbAWSc4Lknid5z5HsR96ZqCHYqkAkJIfFJoVbJatbsh2+nuI3wso54G//d+Uc3XyiNGeaXLZuZg+1tkrjSQ4PW1lZZuvBTqTYU2S32OIaTp/O1tRkLlKmpR155cLBbRsaejmgDEnp8VW7FQiZF8Qlx45SqjO+F0Bf/dvCsdp2oElPrt4jXzCib/35Qz299EQVZXRf6joQ/KGwNuw1q2kK013DbqDsjJEZ2l3bqtZgWFsPNGliQe9/iA8l0WhU2yrN78GjMt3MfEDSsFoM3fz5SZpc5NX3n1gnfyii1eV1OucPb+vur83RrOLeJ3ea/SFd/ZdVam6rbjhvZhELW8ARsFstuv1LsxT86yq9sqlSrcGwLn/gff3wrElqaA1pZ3WzyqqbVV7don0NvsN+93faLPrDV2bL7Rha7++AgbJwQq68LpsafCG9/HGFWgPhIVeN8/z6vTrQNudv0eQC2mNhSJtY4NWjV83T02v26hcvbNKBRr/8oYh+u+wT/WP1bv3knMk6bWL+gJ2/yR/SVX9ZFavAvXDWCH3t+OIBOx8QDw6bQwcCTdpVX65JuZPMD7oKzDlA4ZZOkzIxzlLJliO1rJLCdZLVI1nTzT+WDMnqlSx96wjRGDSrNwzDkKJByTlCndVR7LM2a0yHxkGh7N6vMflDfmWnZHe9QUuL5HLRdm4487Z9XUUikuXQr0ObxSa3Pb43hfepoiizrSyuoqLiiPc5uK3Hc/iQMAwv0WhUj7YNa5Skr80beot2hmH28T94F9KBRr++9dCHavaHetiz/60qq429wTxhbE7cz59oM0Z1aD+3K/nbz+1v8McWvccOo+oxDB/nzRyhJ64+QUXp5t1X+xv8uuSPK/X4h7t62PNQ0WhUNz25Xp+2JVYn5Ht024XT4j4UEhiqHDaL7vjqbJ0y3rxTszkQ1o+e2qD/emmz/v7BLq3cXqO99YcniawWQz8/f6omFSb3jRtAbzhtVn1+WqEkqSUQ1iub9ic4ot6JRqN64J2y2PNvnlSauGCAfmIYhs6fNUKvffcUXXFSaawd3c7qFn3zwQ/1rYc+UHl1/7eji0aj+v4Ta2PtxCcWePSLC3iPiuTgcXhUVlem5oNVRQ6vlFIgBY9grcbqkTwLpYzzJc/nJPexkvMYs2qoj0kiSaryNcppsUuRoGTYJFvn69N7bE0aU2G+wfWlWBVN7f25o4rK6+zmvXBtrZSbKxUU9PrYSBJer5SSIvk676Di6a5t4QDoU6LomGOOkSS99NJLR7zPiy++KEkaO3ZsD1si2b23o0Y7qswfGieMzVZJztBc7HbarLr70jkqzjLvptu4r0H/8fc1Ckf6dwB7TzoOmT5lwpGX3SaL6SMzYo/X7K5LWBzxsv1Ae6/csQwHR5KaNjJdz3ynvSVIIBzR955Yp58+8/ERDwF/8N0yPbu2rcWp06a7vkZ1A9BbTptVf7x0jk4a1/mNKJluu2aMytB5M4v076eN0/98YYZeueEUffHYUXGOFBj8hnL7udXltbF5oFOKvDquhBauSB4el10/PnuyXvj3k3V8aXs7ulc2Ver03y7Xr17crEZfsN/Od99bO/TC+rYbqV02/fHSOUOuwhDoisfpUWOgUbvqO9zk5x5h/h2J/43VB7WGA2oM+eSyOcy2c3aPZOu8MrahtUHettExrTmpRzT/qKNAJCC7xd79fKKGBumYYyQr1/6wlZ5uJoq6mFPksse3crtPKyWLFy/Wu+++q3vuuUff/va3NWnSpG63//jjj3XvvffKMAwtWbKkL6dGEuhYTXTJcUN7ISEr1aE/feNYXXDnu2r0hfTKpv36r5c264ef7/6a6E8HE0WGIZ3cxUJOMps6Il2GYba8XTcMEkXbOiaK8kgUIXnlpDn18LeO18+e+1gPrzSHoj74bpm2VDTqjq/OVlaqQ+FIVE3+kJr9ITX5Q2r0mX/vqW3VL57fFDvWb74wQ2NIrAJHxWW36r6vH6uHV+6ULxjW6OxUlWSnqjjbrfSU4dXuFuiL40uzle91an+DX8s/qVRdS0AZbkeiwzoif3q7LPb48hNLqXxAUppQ4NHfvz1Pz6zdq188v0mVjX4FwhHdvXybnli1W99bPF4XzxkVqzw6Giu3V+tXL22OPf/9JTM1mpmzSCIWWeSxe7SjboeK04vldrglZ57kyDSripzdtGMbQE1Bn3zhoLw2lxRqlhwZkjq/lq1V7SNTjOz0riYmdckX8inFntJ1oigUMluNjRra66HoI5fLTBZVVXX+cnfzrQZAnxJF11xzjX7961+rpaVFp512mu69916dffbZnW77zDPP6KqrrlJra6vcbreWLl3al1NjiKtvCeqF9Wazzwy3XYunDP0yy3F5Ht311Tn6+gPvKxyJ6p43t2tMTqq+NHfgewzvb/Bpc0WjJLOyJjN1aPyy2Z/SnDYdk5emT/Y3afO+RvmC4aSe23OwRYEkjRmi1XjAkXLYLPp/50/TlKJ03fL0BgXDUa3YXq0Tf/WaDEOxtpvdueqUMVoydej/rAESyWW36lsnj0l0GMCQZrUYOmd6ke57e4eC4aheWF+hrwyBmSR761r10sdm9UNOmkPnzChMcETAwDEMQ+fNHKHPTcrXna9v1X1v71AgFFFVk18/+Md6/XnFTt1y9mQdP6b3i90V9T5d+8jqWAeS75w2Tp+bNHBzkIBE8bg82tu4V+X1uzQxd4JksUnuUVLtGvMO3wTcbNAYbFUkEpE1GpIsdsnedVs4T1V7y0lrVqZ6WwflC/qUn5Yvu7WLG6rq6qSMDOYTQcrPl8rLO30pxRrfRFGfWs/l5OTo7rvvVjQaVWVlpc477zwdc8wxuvzyy/XDH/5QP/zhD3X55Zdr3LhxuuCCC7R//34ZhqG77rpL+fn8IBzO/rlmj/whs23QBbNGJM2C/knH5OjWc6fEnv/nPzfo3a2dZ4X705sd284dM/yqiQ462H4uFIlq476G7jce4qgownD05bnF+tuV85STZvaHbg2GjyhJNH9Mtr63aMJAhwcAwBE5b+aI2OOn1+xJYCRH7s8rdsYWtr9y/Gg5bcnx+xvQnTSnTd9fMlGv3nCKzuxww9HHext0yT0r9W9/XaVdNUc+vygQimjpI6tV1RSQJJ18TI6uO318v8cNDAYWWZRmT9OOuu1qCbRdJ65CyZoihft/7teRqPE3y2qxtLWd80qWlE63azaCKtrf/ntmOKf3MzeDkaCy3d0kk2tqpDFjJHd8W4thEMrOlsKdr2ukOuJ7Y3ifm/R/9atfVSQS0TXXXKOWlhZt27ZN27dvP2SbaNt029TUVN1111362te+1tfTYgiLRqP62/vtmdIvHTf476Drja/NG63tB5r1p3d2KBSJ6uqHV+mfS08c0HZHw30+0UEzRqbriVW7JUnrdtVpdnHy9k3fVmkmitJT7MoehhVkGL6OLcnSs985Ubc+s1Hr99Qr1WlVmtOmNJddHqet7bldaS6bPE6bstMcWjK1QDZrn+6NAQCg30wd4dWY3FRtP9Cs98tqtLeuVUUZnS9WDQatgXDs9ze71dDX5iXX729AT0ZluXXX1+Zo5fZq/ezZjbGbEl9YX6FXNlXqypNL9aXjilWUkdJtS7pfvrBJq3bWSpJGZKTo9i/N6lMLO2Cw87q82tu4V7sadmlCzgTJ4ZFSCqXmMskW3wXwcCSimkCTUqx2SX6zDV4X9lqbNGmfuZbdkmJR1O3s1bmibY3qUrtqOxeNmq3nSkp6dVwkqfR08+9OKu0slviuY/TLNOdLL71UZ5xxhv73f/9Xzz//vDZs2BBLDlksFk2bNk3nnHOOrr32WiqJoHW762Nt0mYVZ2hCgSfBEfW/H501SWXVzXptc6UafCF9+y+r9NJ/nDwgC5XhSFRvfWpWLXldNs1oq6oZjmaMyog9Xts2aDcZtQRC2lvvkySNzU2lPzyGncL0FN196ZxEhwEAwFExDEPnzRih373yiaJR6bl1e/XtBWMTHVaXnvpoj+pbg5Kkc6YXKc8T3zYowGAxb0y2nv3OSXpi1S795uUtqmoKKBCK6I7Xt+mO17fJZjE0MjNFo7LcGp3tVnGWW8VZqSrOcmvD3no9+G6ZJMlhtejOtlmbQDKzyKJUe6p21JVpVHqx3PYUyT1Cai43q3qs8btJoinkU2soIK/FkCxOydZ1lVBda5085pKLGnJT5Ozlmos/5JfD5pDH3kWiqLFR8nikESM6fx3Di9crOZ1SIGD+nUD9kiiSpIKCAv3yl7/UL3/5S4VCIdXU1EiSsrKyZLP122mQBP7+wa7Y4y8dl5xD26wWQ//75Vm6+K53tbmiUVsrm7Rie7VOPqb/q33W7a6L/eJ20jE5w/qu+YkFXjmsFgXCEa3dVZfocAbM9g7zicYOYKUaAAAABsa5M4v0u1c+kSQ9vWbwJoqi0agefHdH7PnlJ5YmMBog8awWQ5ccV6zPTyvUH17fqgfeLlMgbLbVD0WiKqtuUVl1i976tOtj3HrelENucgSSWborXXsa92h3/S6NzxkvufIlz1ipYbPZis4SnzXjhmCrApGQHIYkR55k6TpRG65pv/H4aNrO+UN+pdhSum4bVldnzqXJyur1sZGEvF4pJUVqaUl4omhAVpRtNpvy8vKUl5dHkgiHaPaH9ExbH+5Uh1VnT0/eoW1pTpv+43PHxJ7/86O9A3KeQ9rOjR++beckc+D91BHmD/HtVc2qbPQlOKKB0XE+0UC2NAQAAMDAKM1J1YyRZquRj/c2aGtlY4Ij6tw7W6v1yX7zveexozM1rS1mYLjzuOy6+cxJWnbDAv3bwrFaPCVfkwq9SnV0P7/rC3NGJu0Ns0BnDs4q2l63Q63BVsmwSN5JUsoIyV9pttuKg8ZgqxSNmh1Z7N2PKXBXta+5ODN7n8xpDbcqMyVLVksX3w9aWsz5RHSHgSSlpkppaVJra6Ij6b+KIuBIPL9un5rbBo+fO7NIqc7k/hI8dWKePE6bGv0hvfxxhX4RnCqXvX8Hv77ZIVG0YJgniiSzHcDq8jpJ0srtNTp3RvIlI7cdUlEU376+AAAA6B/nzhwRa5f8xKo9uunMiQmO6HB/eodqIqA7o7NT9f0l7dduNBpVTXNA5TUt5p/qFu2sadGumhaNzUvTLWdPpnU4hp10V3psVtH47PGS1SFlTJOqmqVAleQc+LWsKl+jnEZEsrole9cjMKKKKrsyEHtuy85Ub1NZoUhIma6Mzl+MRMzkGKNZcJBhSHl5UmVloiPpv0TRp59+qj//+c9asWKFKioq1Nraqpdfflnjxo2LbbNhwwaVl5crNTVVp5xySn+dGkPI3z4ojz3+0nHJPwTVZbdqydQCPb5qt5r8Ib2yaX+/VlHVtwS1pq3F2vj8NBWmD94huPEyf2y27nxjmyRp5fbqJE0Utd/dMjaPiiIAAICh6JzphfrlC5sUjkT1p7d36PxZRZpY0PsWNwNlR5U5c1WSitJdWjyFRS2gJ4ZhKDvNqew0p2YVd1+1AAwXB2cVba/doeL0YrlsLsmRbiaLqt+Tgo3dJm/6qjUcUGPIJ5cikiNTMrpeDm8w/BpdYbaTbHIbirp71wosEo2YVVSOHuYT5eX16rhIcnl55oyiBOtz67lIJKIbb7xRkydP1i9/+Uu99tpr2rhxo8rKyhT4zD+wvLxcZ599ts444wzt2bOnr6fGELOlolEftVV6TCzwaPowaVtw/qz24XRPr+nf9nNvb61SpO3WhgUDMP9oKJozOlM2i3mH1srt1QmOZmBsqzQTRTaLoeIsd4KjAQAAwNHI87p0xUlmlU4gHNF1f18jfyic4KjaPfRuWezxpfNLhvUsVABA36S70tXgr9fu+t3tH3QXSd6JUrBeigzcInlT0CdfyK8Ui12yd78WWdVaK0/bFIOaPFev28P5wn45bU6lObpIfDU0SDk5UkZGr46LJOcdHDcK9fmd3lVXXaXf/e53CofDKioq0sUXX9zltp///OdVWlqqcDisJ554oq+nxhDz9w7VRF+eWzxsyq3njclWnse8A+GNLZWqa+m/H37LP2kvSzxlAokiSXI7bLHBoNsPNGt/Q3LNKYpEotpRZbaeG53tlp1f2AEAAIas7y4ar4kF5mLS5opG/XbZJwmOyNTgC+rxD3dJklx2i748l5kqAICjd7CqaFvtNvlCHdZpPMdIqSWSr1KKRgbk3I3BVkXCrbLaUyVb911ZAjW1scf+nN53cPGHfHLb3XLbu+j409zMfCIczuuVbDYpGExoGH1aYXz11Vd1//33S5J++MMfqqysTI899li3+3zhC19QNBrVa6+91pdTY4jxBcN66iOzisxhs+j8mSN62CN5WC2GzmlrfxYMR/XC+op+OW40GtXytvlELrtFx5X0fsBespo/Jjv2ONmqivbUtcofMt88jc2l7RwAAMBQ5rRZ9btLZsrRdvPPPW9uHxTvXx//cHdstuyFs0cqw+1IcEQAgKEu3ZWuen+9djd0qCqy2KSMqZIzx0wWDYAaf7Os0bDkzJaM7ueG26sb20PLyuj1uXwhn7LcWbIYnSy5R9oSYQUFvT4uklx6upSSIrW2JjSMPiWK7rnnHklmpdD/+3//T1Zr9xebJM2dO1eS9PHHH/fl1EmnsTWxGcOB9vLHFaprMf+Nn59aoHS3PcERxVfHxNjTa/qn7eIn+5u0v8Evyaxactl7vv6Gi3mHJIpqEhhJ/+s4n2gMiSIAAIAhb1KhV99dNF6SOd/6u4+tVYMvcb8fhiPRQ9rOXX5CScJiAQAkD4ssctvd2lK1RdtrtysUCZkv2NxSxnTJ4pACtd0fpJfCkYhqfHVKsTmPaA5SemX7Qr0ns/edeyLRiDKcGZ2/2NQkpaUxnwiH83ik1FSppSWhYfQpUbRixQoZhqErrrjiiPcZOXKkJKmion+qKpLFm59WJTqEAfXoB7tij780tziBkSTG1BFejclNlSS9t6NGe+v6niE+pO3ceNrOdTR7dIbs1uScU7TtQHPs8di2rykAAAAMbd86eYzmlpodAvbUterWZzYmLJbXNleqvMZcqDj5mBwdkz9wA8YBAMNLpitTkrRq7yqt3L1SVS1t66GuHCljihRuNf/0k6aQT62BJrkc6ZKt+zWUSDSiokozedXgNmRP6aJ9XBfC0bAshkVpji5u6m1okLKzmU+Ew1ks5uyqoVxRVFlpLlSXlJQc8T52u1lJEgqF+nLqpPPqlgOJDmHA7Kxu1rvbzMX60pxUHV86/FqkGYZxSFXRM2v39vmYb37SnlxcQKLoEG6HTTNGZkiSdlQ1q6I+eeYUdawoGptHRREAAEAysFoM/c8XZijNaZMk/WP1br24fl/c4whHorp7+bbY88tPLIl7DACA5GXIUKYrU3mpedrftF/v7HpH6/evV2uwVUodbc4s8ldLkb6vG0ejUVX6GuQP++Rw5amnZfCG1nqltS0f7c+393qOkD/kl8vmUmpXiaLGRuYToWv5+ZIvseuXfUoUpaaamdgDB448ybF7t9mHMitr+CULuvPOtho1+5MzedaxmuiS40bJGKbfEM9tm1MkSU+v6VuiqCUQ0vs7zJZqIzNTNCaHypLPmj+2vf3cezuSp6poW2WHRNFRDFYEAADA4DQqy62fnjsl9vyHT61XZUN8Fwx+t+wTrdpptv0pzUnVwvG0xwEA9D+7xa7CtEKlWFO0qWqT3tn1jnY17FHEM0FKKZL8+6Vo5KiPH41GtbVhv9ZXb5fH7pZh9/a4T0tN+9pRU4671+dsDbUqxZ6iFJvr8BeZT4SeZGSYPYgTqE+JojFjxkiSNm488rL4F198UZI0ZcqUHrYcXgKhiJZ/knxVRaFwRI+vMpODNouhi2aPTHBEiVOSk6qZozIkSZv2NeiT/Y3d79CNldurFQibP2QWjM8dtsm37hw6pyiJEkVtredy0pzDbtYXAABAsrto9ggtmWIuItW2BPX9f6xTNE6LBi9/XKE/vL5Vklnh9IsLpspi4fcMAMDASXOkqSitSM2BZr235z19uH+d6lNGS45MyVchte6Vgg1SJHzEx4yEA9pc/YnWVW6QWyGlu/Ml2xG0kauub3+cnd7rf4s/7Fd2Snbna3RNTeYcGuYToSter2S1SuEj/1rvb31KFC1atEjRaFR33HGHIpGes7wbN27Ugw8+KMMw9PnPf74vp05KL25IvrlNr22u1IFGvyTp9En5yvU4ExxRYp0/s72q6J8f7Tnq43RsO8d8os7NLs6MzSlasS05EkX1rUFVNZnXE/OJAAAAko9hGPrlhdOUk2b+3vTGlgP663vlA37erZVN+u5ja2PPbz5zok4YmzPg5wUAwGJYlOvOVXZKtnbW7dTb+9bpEyNfgcxjpbSxkqJmhVHrXilQ13lbukhA8tco0rxbGw9s0oaGSnk8xfJmTZPSSiX1fONDalX7TOiUrOxutuxcNBpVurOLBNPB+USZmb0+LoYJr1dKSUnonKI+JYr+/d//Xampqdq2bZuuvvrqbucOLVu2TIsWLZLP51NWVpauvPLKvpw6Kb22ab98wcRlDQfCIW3n5o5KYCSDw1nTi2Rtuyvv6TV7FYkc3d2BB6vPbBZDJ4zt/Q+v4SDFYY1VcJVVt2hffWIHwvWHTfsaYo+ZTwQAAJCcslId+s3F02PPf/H8Jm3vMKeyvzX5Q7rqLx+qqa0V+tnTC3XFSaUDdj4AADrjtDpV5CmSxbBoTdUmvV21XbusuYrknSrlniR5JkiGTfIfkFr3SIFayV8lteyWArUKWZxaH07RpkiKMrNnKi19vFmZZNh6Pnk0qtzKoCSpNlXKdmb0KvZQJCSLYVGqo4ubehsbpdJS5hOha0M9UZSfn6+7775bknT//fdr7Nix+rd/+7fY67fffru+/e1va8qUKVqyZIn27t0ri8WiBx98UGlpLHJ+VnMgrLc/rep5wyFiX32rXt9SKUkqSndpwTFUvuR6nDpxnHln3p66Vq0ur+31McqrW7Sj7S6H2aMz5XHRfqwr8zu0n3tve00CI+kfHf8Ns4u5CwUAACBZnToxT189vliS1BoM6/rH1ioUPvpZDV2JRqO68bG1sfbGE/I9+vXF02ltDQBICEOG0p3pKkwrVIOvQe/tWamV+1brQMSqaMZUqeBUKe8kKX2KZHVKdq+UOVPB7PlaG83UFl+rMj2j5HZ4enXeaFOLUn3mzdx7822y9XLJ3Bfyy2VzKc3RyXp3JGImiJhPhO7Y7VJW1tBNFEnSV7/6Vf3tb3+T1+vVrl279Mc//jH2pvK+++7T/fffr02bNikajSotLU2PP/64zjrrrD4Hnqxe+jh52s898eFuHSyY+cKxo2KVNMPdIe3n1vS+/dzyT9tnWdF2rnsd5xQlQ/u5jrOWji/NSmAkAAAAGGg/OmuSSrLNYdprd9XpNy9vOeqOBF25e/n22O+gHpdNf7x0jtyOI7jzGgCAAWQ1rMpLzVNOSo72Nu7Vu7ve1ZqKNWoItEqufCl9spR3mpS3QAF3iT6qLde2uu3KScmR+0jmEX1Gc037ektdrqvX+/vDPqU6UpVi7+TcTU1SairzidCzgoKhnSiSpC9+8YvaunWrbr31Vs2ZM0dWq1XRaDT2Z8qUKbr55pu1detWXXDBBf1xyqTjdpifimUb9ys4AHeKxVs0GtUTq3dLMpPmXzh2ZIIjGjwWTSmQy25+vp9ft6/Xn+/lW0gUHanZozPlsJr/r1fuGNqJIl8wHKtAG5mZolFZ7gRHBAAAgIHkdtj0u0tmxm64++Ob2/Wle1eqrMMMhb5469MD+s3Lm2PPb//STJXkMAcTADB4OKwOFaUVKdWeqk9rPtVb5W9p84Et8oV8ksUiXzigVRWrtaN2h/LceXLZep/kkaRQTXvHn0BO76qRJMkf9is7pYvREA0NZqVIFjf8ogeZmWYFWoL0S6JIkrKzs/XjH/9Y77//vnw+nyorK7Vv3z75/X6tX79ev/jFL5RH5rRLJ7e1I6tvDR5SNTBUrS6v1c7qFknSCWOzNTKTRe2D0pw2nT4pX5JU2xLUWx0qhHoSCEW0YpvZnjAnzaHJhd4BiTFZuOxWzSzOkCTtrG7R3rqhO6doza46+UPmD4uOLfUAAACQvGYVZ+oHSybEnr+/o0ZLbn9T9721XeE+VBftqmnRv//to1gHiOtOP0anTczva7gAAAwIt92tEZ4RshgWratcq7d2vqXttdu1au8qldfvVEFagRxWx1Ef31nVPgvQkdX7Vv/RaFReZxdrdMwnwpHytn0NJShZ1G+JokMOarEoJydH+fn5stuZn3IkPjexvTLkxQ1Dv/3cP1a3t1S7cBbVRJ91/swRscf//GjvEe+3ametmgNhSdLJx+TKQju/HnVsPzeUk7AdY59HoggAAGDY+PaCsfrrt47XyEyznY0vGNH/e36TLr77XW2tbOz18XzBsK756yrVtphDuz83MU//ftox/RozAAD97eD8oqK0IvlCPq3au0p7GveoMLVQdksf1p+jUWUe8EmSatKkHGdGr3YPRUKyWqydzyeKtt2RUVh49PFh+EhPl1JSJJ8vIacfkEQReu+Esdly2sxPx78+3t+nu8MSzRcM67m1ZvIjxW7VkqkMa/usBeNzleE2f4gt27hfzf7QEe33JvOJem3emPbS3mRJFB0/hnJlAACA4eTEcTl6+boF+sYJJbGPfVRep8/f/rbueH2rQkfYzjoajepHT23Qhj0NkqSSbLd+e8lMbkADAAwZFsOi7JRsFaQVqMhTJJulb7P1LI2tSvGb67Bl+YZywr2bceQL+eWyupTq6KR9K/OJ0Bter5koStCcIhJFg0Sq0xZb+K9q8mvVztoe9hi8XttcqQafmfg4c2qBUp0MQ/0sh82is6aZdxO0BsP618YjqyI7OJ/IMKSTj8kZsPiSyeziDnOKttckOJqjY84nqpMkjcpKoZUjAADAMJTqtOmn507RY1fNV0m2+X4wEI7oNy9v0QV3vqtN+xoO2ycUjqiy0afNFQ16d2uVfrvsE/2jbZZsit2qP156rNJT6AICABh6rIZVln5Y2o5W18ceH8h3yqLe3TzhD/uU5kjrfD5SQ4M5d4b5RDgSLpdZVZSgRNERreCfdtpp/X5iwzD06quv9vtxh7IzpxXoXxv3S5Je3LBPc0uH5jeRJ9t+8ZCkC2fTdq4r580cob++Vy5JenrNXl3QQ4u+ykafNrb98je1KF3Zac4BjzEZuOxWzSrO0Hs7alRe06I9da0akdG7u0MS7aPyOgWYTwQAAABJc0uz9OJ/LNDvXvlE9721XZGotH5Pvc79w9s6+ZhcNfqCqm4OqKY5oLq29nKd+fXF0zWhoPcDuwEASCaBmvabiltzen9jrj/kV3ZmF2s1jY3SzJmShVoNHKH8fKm8PCGnPqJE0RtvvCHDMBSN9r0d2sHjGAzwOsxpE/NltxoKhqN6eUOFbjl78pD7/1TV5NcbbVUvBV6X5o9lUbsrx47O1IiMFO2pa9Vbn1apqsmvnG6SP299UhV7TNu53pk3Jlvv7TB/8K/cVq2L5gytBCbziQAAANBRisOqH35+ks6cWqDvPbFOWyubFAxH9drmyiPa/8qTS3XOjKIBjhIAgMHPUt1ekWtkZfR6/4gi8jg7ufEiGjX/MJ8IvZGdLYXDCTn1ESWKFixYMOQSFkNReopdJ4zN0fJPDmhvvU/rdtdrxqiMRIfVK8+u3atQ23yl82eNkJVe112yWAydO7NId72xTeFIVM+v26evd+g5/lnLP2mfT7SARFGvzBuTrdtf/VSSmXQZyomi40kUAQAAoM2s4kw9/+8n6f9e3ao/vrlNwbD5u1ia06asVIeyUh3Kbvs7K818XJKdqtMn5Sc4cgAABoFoVJ4qs81XTZqU5UyXAke+eygSks1i63w+UXMz84nQe+np5t/9ULDTW0dcUYT4OHNqQSwh8OKGiiGXKHpy9Z7Y4wtnj0hgJEPDeW2JIkl68qM9mjcmW5WNPlU2+LW/7e+Dz9ftMXumepw2zSrOSGDUQ8+s4gw5bBYFQhGt3FHd8w6DiC8Y1ke76iRJxVnuIdc2DwAAAAPLabPqxsUTdM3CsapvDSor1SGX3ZrosAAAGPQsja1y+s1W/9sLDI0IdZLw6UZrqFUum0tp9rTDX6yvN+cTZXPDL3rB65WcTinQi4xlPzmiRBHi54zJ+frhU+sViUovbdinHyyZMGSquT7Z36j1bcmMaSPSNT6fftc9mVjg1cQCjzZXNGrtrjot/v2bPe5z4rgc2a30Nu0Nl92q2cUZWrm9RrtqWrW7tkUjM3vfdzYROs4nmjdmaM4tAwAAwMBLddqU6uRXfAAAjpS1qr3t3K58i8ZEejcP3B/2K9OVKaetk/2amqQZM5hPhN7xeqWUFKmlJe6n5it1kMlOc+r4UjPTXFbdos0VjQmO6MhRTXR0zp91ZP+vMtx2zRmdqRsWjR/giJJTx9k+K7fXdLPl4LKiQ9s5Zn4BAAAAAAD0D2N/+/pQXX6KDPXuZn1/yK+slE5u6o1GzTkzzCdCb6WmSmlpUmtr3E/N7UaD0JKpBbHF4Zc2VGhSoTfBEfUsHInqnx+ZiSKbxWAwai9844QSlde0aFdNi/K9LuV5nLG/87xO5XlcyvU4aR/RR2aiqH1O0cVDZE7RIfOJSkkUAQAAAAAA9Fk0Ktsec80laJX8BelSL9fmo4rK4+yko9LB+UT5zARELxmGOdeqslJyueJ66iNOFN1www2SpJtuukl5nQzhCofD2rPHTBQUFxd3eZzt27fr4osvlmEYWrVqVW/jHRYWTynQT575WJKZKLr+jMFfQbJiW7UqGnySpIUTcpWT1rtSzeHMZbfqlxdMS3QYSW/mqAw5bRb5QxGt2DY05hT5gmGtKa+TJI3OdquI+UQAAAAAAAB9ZmlslbPRL0naPNJQntG7G/UDkYDsFrvSHJ3MJ2pokLKymE+Eo5OXl5AZRUfceu73v/+9br/9dlVVVXX6+ubNm1VSUqIxY8Z0e5zW1latWbNGa9as6VWgw0lBukuzizMkSVv2N2r7gabEBnQEnly9O/b4wtlDo1IDw4s5pyhTkrSnrlW7auLf67O3VpfXKhBum09ENREAAAAAAEC/sO9pv4l4XamhkmDvZq37Q365bC6l2TtJFDU2SqWlzCfC0fEmprtYv3+1RqPR/j7ksHTm1PYeli9uqEhgJD1r9odiMXpdNp028fCKM2AwOHRO0eCvKlrZofJp3thOet4CAAAAAACg12x729dcNpdYVRzsJOHTDV/IJ4/TI4fNcegL0agUiTCfCEfP65VsNikYjOtpSWsOUkumFsQev/zx4E4UvbShQq3BsCTp7BlFzNLBoDVvTHuyZeX2mm62HBw6xtgxyQUAAAAAAICjFInIuq9WklTvlhwZGbL2cpk8EA4oO6WTtZqWFsntNtuHAUcjPV1KSZF8vrielkTRIDUqy60pRWaZ2brd9dpdO3jbZD35UXvbuYtmj0hgJED3Zhabc4oks6JoMFdAtgbCWrOrTpJUku1WYTrziQAAAAAAwOAT3F8p4901SjkQ34Xto2U90CBr203v60oMTQgeXReXNEcn7erq66WMDOYT4eh5PFJqKokitDuzQ1XRS4O0/dzeula929Yea3S2OzYDBhiMnDar5oxun1O0u7Y1wRF17aOO84moJgIAAAAAAINQuK5OW087TdZnXlfqvhaz9dog99n5RBMCGb3aPxAJyGaxKc2ReviLB+cTWem4hKNksUg5OVJrfNctSRQNYks6zCkarImif67ZE/v+f+GskTIMI7EBAT3omHRZMYjnFHWMjUQRAAAAAAAYjKwZGXLPmiVJsvnCstQ1JziinnWcT/TpaLtGhno7n8gvl82lNMdn9otGpXBYKirqjzAxnOXnS4FAXE9JomgQG5eXpmPyzG84q8prVdkwuMo3o9Gonly9J/b8glm0ncPg1zHpsnLb4E0UrSRRBAAAAAAAhgDP4sWxx46y/QmMpGeGPyhbVYMkqTxXynNmyqLe3fjuD/mU7kyX3Wo/9AXmE6G/ZGRIcS7IGJKJottuu03HHXecPB6P8vLydP7552vLli2HbLNw4UIZhnHIn6uvvvqQbcrLy3XWWWfJ7XYrLy9P3/ve9xQKhQ7Z5o033tDs2bPldDo1btw4Pfjgg4fFc8cdd6ikpEQul0vHH3+83n///X77ty5paz8XjUovbxxc32jX76nX1somSdLckiwVZ7sTHBHQsxmj0uWyD+45RR3nE5XmpKog3ZXYgAAAAAAAALrgWXRG7LGjrDKBkfTMtq9GRttS0NpSQxN72XZOkgLhgLJSOplr1NBgLvDn5PQpRkBer+SK73rgkEwULV++XEuXLtXKlSu1bNkyBYNBLVq0SM3Nh5Y2Xnnlldq3b1/sz69//evYa+FwWGeddZYCgYDeffddPfTQQ3rwwQd1yy23xLbZsWOHzjrrLJ166qlas2aNrrvuOn3rW9/Syy+/HNvm0Ucf1Q033KCf/OQnWr16tWbMmKHFixersrJ/viku6TCn6KF3yxRsm1kyGHSsJrpwNtVEGBo6zinaW+/Tpn2NCY7ocKvLaxUMm+9a5o05uoGKAAAAAAAA8WDPz1d0tDlCw1rXPKjbzx0+n6h389ajbf+lOTtpV9fQwHwi9A+vV0pJiespbb3d4c4771ReJ+VzHRMjP/vZz7rcvz8SKC+99NIhzx988EHl5eVp1apVWrBgQezjbrdbBQUFn91dkvSvf/1LGzdu1CuvvKL8/HzNnDlTP//5z/WDH/xAP/3pT+VwOHT33XertLRU//M//yNJmjRpkt5++2397ne/0+K2ksrf/va3uvLKK3X55ZdLku6++249//zz+tOf/qSbbrqpz//WyYVezS7O0OryOm2tbNJfV+7UN04s7fNx+yoQiuiZtXslSQ6bRZ+fXtjDHsDgccakfL2z1XxjcM+b2/T7L81KcESHou0cAAAAAAAYSvxTx8i1c58ks/2cb+aYBEfUiWhUtrZEUcAq7S5yqLCudx2SAuGAHFaHPA7P4S9GIlJubn9EiuHO65WczriesteJorvuuqvL14y2vnm33nrr0Ud0FOrr6yVJWVmH3nn/17/+VQ8//LAKCgp0zjnn6Mc//rHcbvPiX7FihaZNm6b8/PzY9osXL9Y111yjjz/+WLNmzdKKFSt0+umnH3LMxYsX67rrrpMkBQIBrVq1SjfffHPsdYvFotNPP10rVqzol3+bYRi65ZwpOv+OdyRJv3vlU503c4QyUx39cvyjtfyTA6ppNgdqLZqcL6/L3sMewODxxeNG6fZXP1VtS1DPrtun7y6aoFFZg6d14optJIoAAAAAAMDg1xRo0p83/lnPlazW/7R9zL6zclAmiiwNLbI2+yVJm0YZKo1myuj1fCK/XDaXUh2ph74QiZgzZdLT+ytcDGd2u5TZu2q3vupVomgwzvKIRCK67rrrdOKJJ2rq1Kmxj3/lK1/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" ], "text/plain": [ " mase mae mape rmse smape\n", "MSTL 0.341926 709.932048 2.182804 892.888012 2.162832\n", - "NeuralProphet 1.089964 2263.060303 7.310210 2681.742759 7.651353\n", - "Prophet 1.107472 2299.413375 7.427523 2742.792022 7.789355\n", + "NeuralProphet 1.084915 2252.578613 7.280202 2671.145730 7.615492\n", + "Prophet 1.094768 2273.036373 7.343292 2709.400341 7.688665\n", "SeasonalNaive 0.894653 1857.541667 5.648190 2201.384101 5.868604" ] }, @@ -2884,7 +1991,7 @@ "id": "J8yEigSp2OGu", "metadata": {}, "source": [ - "With respect to numerical evaluation, `NeuralProphet` improves the results of `Prohet`, as expected, however, `MSTL` improves over `NeuralProphet`'s foreacasts by 68% (`MASE`)." + "With respect to numerical evaluation, `NeuralProphet` improves the results of `Prophet`, as expected, however, `MSTL` improves over `NeuralProphet`'s foreacasts by 68% (`MASE`)." ] }, { diff --git a/nbs/docs/tutorials/UncertaintyIntervals.ipynb b/nbs/docs/tutorials/UncertaintyIntervals.ipynb index 0fb0f94b2..224deec67 100644 --- a/nbs/docs/tutorials/UncertaintyIntervals.ipynb +++ b/nbs/docs/tutorials/UncertaintyIntervals.ipynb @@ -1,11 +1,26 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "cd985567", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "import warnings\n", + "warnings.simplefilter('ignore')\n", + "\n", + "import logging\n", + "logging.getLogger('statsforecast').setLevel(logging.ERROR)" + ] + }, { "cell_type": "markdown", "id": "14f5686c-449b-4376-8c58-fc8141f4b0f8", "metadata": {}, "source": [ - "# Probabilistic forecasting \n", + "# Probabilistic Forecasting \n", "\n", "> In this example, we'll implement prediction intervals " ] @@ -17,27 +32,19 @@ "source": [ "::: {.callout-warning collapse=\"true\"}\n", "\n", - "## Prerequesites\n", + "## Prerequisites\n", "\n", - "This tutorial assumes basic familiarity with StatsForecast. For a minimal example visit the [Quick Start](https://nixtla.github.io/statsforecast/examples/getting_started_short.html) \n", + "This tutorial assumes basic familiarity with StatsForecast. For a minimal example visit the [Quick Start](../getting-started/1_Getting_Started_short.ipynb) \n", ":::" ] }, { "cell_type": "markdown", - "id": "9105f346-d216-48be-91e0-cfb1e38a60c1", + "id": "2a041b37-4438-4da8-a727-e238e3ea9825", "metadata": {}, "source": [ "## Introduction \n", "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "2a041b37-4438-4da8-a727-e238e3ea9825", - "metadata": {}, - "source": [ "When we generate a forecast, we usually produce a single value known as the point forecast. This value, however, doesn't tell us anything about the uncertainty associated with the forecast. To have a measure of this uncertainty, we need **prediction intervals**. \n", "\n", "A prediction interval is a range of values that the forecast can take with a given probability. Hence, a 95% prediction interval should contain a range of values that include the actual future value with probability 95%. Probabilistic forecasting aims to generate the full forecast distribution. Point forecasting, on the other hand, usually returns the mean or the median or said distribution. However, in real-world scenarios, it is better to forecast not only the most probable future outcome, but many alternative outcomes as well. \n", @@ -90,20 +97,14 @@ "::: " ] }, - { - "cell_type": "markdown", - "id": "f4191d2e-70a7-4045-a54f-3d3c1a5e303b", - "metadata": {}, - "source": [ - "## Install libraries " - ] - }, { "cell_type": "markdown", "id": "99f313fe-21c0-4823-9bdd-722869db85ad", "metadata": {}, "source": [ - "We assume that you have StatsForecast already installed. If not, check this guide for instructions on [how to install StatsForecast](https://nixtla.github.io/statsforecast/examples/installation.html) " + "## Install libraries \n", + "\n", + "We assume that you have StatsForecast already installed. If not, check this guide for instructions on [how to install StatsForecast](../getting-started/0_Installation.ipynb)" ] }, { @@ -13936,7 +13937,7 @@ "source": [ "## Train models\n", "\n", - "StatsForecast can train multiple [models](https://nixtla.github.io/statsforecast/#models) on different time series efficiently. Most of these models can generate a probabilistic forecast, which means that they can produce both point forecasts and prediction intervals. \n", + "StatsForecast can train multiple [models](https://nixtla.github.io/statsforecast/#models) on different time series efficiently. Most of these models can generate a probabilistic forecast, which means that they can produce both point forecasts and prediction intervals.\n", "\n", "For this example, we'll use [AutoETS](https://nixtla.github.io/statsforecast/models.html#autoets) and the following baseline models: \n", "\n", diff --git a/nbs/index.ipynb b/nbs/index.ipynb index f30f64af3..a984779e2 100644 --- a/nbs/index.ipynb +++ b/nbs/index.ipynb @@ -57,7 +57,7 @@ "``` \n", "\n", "\n", - "Vist our [Installation Guide](https://nixtla.github.io/statsforecast/examples/installation.html) for further instructions.\n" + "Vist our [Installation Guide](./docs/getting-started/0_Installation.ipynb) for further instructions.\n" ] }, { @@ -82,9 +82,9 @@ "sf.predict(h=12, level=[95])\n", "```\n", "\n", - "**Get Started with this [quick guide](./examples/Getting_Started_short.ipynb).**\n", + "**Get Started with this [quick guide](../nbs/docs/getting-started/1_Getting_Started_short.ipynb).**\n", "\n", - "**Follow this [end-to-end walkthrough](./examples/Getting_Started_complete.ipynb) for best practices.**" + "**Follow this [end-to-end walkthrough](../nbs/docs/getting-started/2_Getting_Started_complete.ipynb) for best practices.**" ] }, { @@ -121,14 +121,14 @@ "## Highlights\n", "\n", "* Inclusion of `exogenous variables` and `prediction intervals` for ARIMA.\n", - "* 20x [faster](./experiments/arima/) than `pmdarima`.\n", + "* 20x [faster](https://github.com/Nixtla/statsforecast/tree/main/experiments/arima) than `pmdarima`.\n", "* 1.5x faster than `R`.\n", "* 500x faster than `Prophet`. \n", - "* 4x [faster](./experiments/ets/) than `statsmodels`.\n", + "* 4x [faster](https://github.com/Nixtla/statsforecast/tree/main/experiments/ets) than `statsmodels`.\n", "* Compiled to high performance machine code through [`numba`](https://numba.pydata.org/).\n", "* 1,000,000 series in [30 min](https://github.com/Nixtla/statsforecast/tree/main/experiments/ray) with [ray](https://github.com/ray-project/ray).\n", "* Replace FB-Prophet in two lines of code and gain speed and accuracy. Check the experiments [here](https://github.com/Nixtla/statsforecast/tree/main/experiments/arima_prophet_adapter).\n", - "* Fit 10 benchmark models on **1,000,000** series in [under **5 min**](./experiments/benchmarks_at_scale/). \n", + "* Fit 10 benchmark models on **1,000,000** series in [under **5 min**](https://github.com/Nixtla/statsforecast/tree/main/experiments/benchmarks_at_scale). \n", "\n", "\n", "\n", @@ -142,21 +142,19 @@ "source": [ "## Examples and Guides\n", "\n", - "πŸ“š [End to End Walkthrough](https://nixtla.github.io/statsforecast/examples/getting_started_complete.html): Model training, evaluation and selection for multiple time series\n", + "πŸ“š [End to End Walkthrough](https://nixtla.github.io/statsforecast/docs/getting-started/getting_started_complete.html): Model training, evaluation and selection for multiple time series\n", "\n", - "πŸ”Ž [Anomaly Detection](https://nixtla.github.io/statsforecast/examples/anomalydetection.html): detect anomalies for time series using in-sample prediction intervals.\n", + "πŸ”Ž [Anomaly Detection](https://nixtla.github.io/statsforecast/docs/tutorials/anomalydetection.html): detect anomalies for time series using in-sample prediction intervals.\n", "\n", - "πŸ‘©β€πŸ”¬ [Cross Validation](https://nixtla.github.io/statsforecast/examples/crossvalidation.html): robust model’s performance evaluation.\n", + "πŸ‘©β€πŸ”¬ [Cross Validation](https://nixtla.github.io/statsforecast/docs/tutorials/crossvalidation.html): robust model’s performance evaluation.\n", "\n", - "❄️ [Multiple Seasonalities](https://nixtla.github.io/statsforecast/examples/multipleseasonalities.html): how to forecast data with multiple seasonalities using an MSTL.\n", + "❄️ [Multiple Seasonalities](https://nixtla.github.io/statsforecast/docs/tutorials/multipleseasonalities.html): how to forecast data with multiple seasonalities using an MSTL.\n", "\n", - "πŸ”Œ [Predict Demand Peaks](https://nixtla.github.io/statsforecast/examples/electricitypeakforecasting.html): electricity load forecasting for detecting daily peaks and reducing electric bills.\n", - "\n", - "πŸ“ˆ [Intermittent Demand](https://nixtla.github.io/statsforecast/examples/intermittentdata.html): forecast series with very few non-zero observations. \n", - "\n", - "🌑️ [Exogenous Regressors](https://nixtla.github.io/statsforecast/examples/exogenous.html): like weather or prices\n", + "πŸ”Œ [Predict Demand Peaks](https://nixtla.github.io/statsforecast/docs/tutorials/electricitypeakforecasting.html): electricity load forecasting for detecting daily peaks and reducing electric bills.\n", "\n", + "πŸ“ˆ [Intermittent Demand](https://nixtla.github.io/statsforecast/docs/tutorials/intermittentdata.html): forecast series with very few non-zero observations. \n", "\n", + "🌑️ [Exogenous Regressors](https://nixtla.github.io/statsforecast/docs/how-to-guides/exogenous.html): like weather or prices\n", "\n" ] }, @@ -168,14 +166,14 @@ "## Models\n", "\n", "### Automatic Forecasting\n", - "Automatic forecasting tools search for the best parameters and select the best possible model for a series of time series. These tools are useful for large collections of univariate time series.\n", + "Automatic forecasting tools search for the best parameters and select the best possible model for a group of time series. These tools are useful for large collections of univariate time series.\n", "\n", "|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |\n", "|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|\n", - "|[AutoARIMA](../models.html#autoarima)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", - "|[AutoETS](../models.html#autoets)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", - "|[AutoCES](../models.html#autoces)|βœ…|βœ…|βœ…|βœ…|\n", - "|[AutoTheta](../models.html#autotheta)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", + "|[AutoARIMA](https://nixtla.github.io/statsforecast/src/core/models.html#autoarima)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", + "|[AutoETS](https://nixtla.github.io/statsforecast/src/core/models.html#autoets)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", + "|[AutoCES](https://nixtla.github.io/statsforecast/src/core/models.html#autoces)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", + "|[AutoTheta](https://nixtla.github.io/statsforecast/src/core/models.html#autotheta)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", ": {tbl-colwidths=\"[75,25]\"}\n", "\n", "## ARIMA Family\n", @@ -183,8 +181,8 @@ "\n", "|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |\n", "|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|\n", - "|[ARIMA](../models.html#arima)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", - "|[AutoRegressive](../models.html#autoregressive)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", + "|[ARIMA](https://nixtla.github.io/statsforecast/src/core/models.html#arima)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", + "|[AutoRegressive](https://nixtla.github.io/statsforecast/src/core/models.html#autoregressive)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", ": {tbl-colwidths=\"[75,25]\"}\n", "\n", "### Theta Family\n", @@ -192,10 +190,10 @@ "\n", "|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |\n", "|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|\n", - "|[Theta](../models.html#theta)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", - "|[OptimizedTheta](../models.html#optimizedtheta)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", - "|[DynamicTheta](../models.html#dynamictheta)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", - "|[DynamicOptimizedTheta](../models.html#dynamicoptimizedtheta)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", + "|[Theta](https://nixtla.github.io/statsforecast/src/core/models.html#theta)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", + "|[OptimizedTheta](https://nixtla.github.io/statsforecast/src/core/models.html#optimizedtheta)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", + "|[DynamicTheta](https://nixtla.github.io/statsforecast/src/core/models.html#dynamictheta)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", + "|[DynamicOptimizedTheta](https://nixtla.github.io/statsforecast/src/core/models.html#dynamicoptimizedtheta)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", ": {tbl-colwidths=\"[75,25]\"}\n", "\n", "\n", @@ -204,7 +202,16 @@ "\n", "|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |\n", "|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|\n", - "|[MSTL](../models.html#mstl)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", + "|[MSTL](https://nixtla.github.io/statsforecast/src/core/models.html#mstl)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", + ": {tbl-colwidths=\"[75,25]\"}\n", + "\n", + "### GARCH and ARCH Models \n", + "Suited for modeling time series that exhibit non-constant volatility over time. The ARCH model is a particular case of GARCH. \n", + "\n", + "|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |\n", + "|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|\n", + "|[GARCH](https://nixtla.github.io/statsforecast/src/core/models.html#garch)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", + "|[ARCH](https://nixtla.github.io/statsforecast/src/core/models.html#arch)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", ": {tbl-colwidths=\"[75,25]\"}\n", "\n", "### Baseline Models\n", @@ -212,12 +219,12 @@ "\n", "|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |\n", "|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|\n", - "|[HistoricAverage](../models.html#historicaverage)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", - "|[Naive](../models.html#naive)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", - "|[RandomWalkWithDrift](../models.html#randomwalkwithdrift)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", - "|[SeasonalNaive](../models.html#seasonalnaive)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", - "|[WindowAverage](../models.html#windowaverage)|βœ…|||||\n", - "|[SeasonalWindowAverage](../models.html#seasonalwindowaverage)|βœ…|||||\n", + "|[HistoricAverage](https://nixtla.github.io/statsforecast/src/core/models.html#historicaverage)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", + "|[Naive](https://nixtla.github.io/statsforecast/src/core/models.html#naive)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", + "|[RandomWalkWithDrift](https://nixtla.github.io/statsforecast/src/core/models.html#randomwalkwithdrift)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", + "|[SeasonalNaive](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalnaive)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", + "|[WindowAverage](https://nixtla.github.io/statsforecast/src/core/models.html#windowaverage)|βœ…|||||\n", + "|[SeasonalWindowAverage](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalwindowaverage)|βœ…|||||\n", ": {tbl-colwidths=\"[75,25]\"}\n", "\n", "### Exponential Smoothing\n", @@ -225,28 +232,28 @@ "\n", "|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |\n", "|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|\n", - "|[SimpleExponentialSmoothing](../models.html#simpleexponentialsmoothing)|βœ…|||||\n", - "|[SimpleExponentialSmoothingOptimized](../models.html#simpleexponentialsmoothingoptimized)|βœ…|||||\n", - "|[SeasonalExponentialSmoothing](../models.html#seasonalexponentialsmoothing)|βœ…|||||\n", - "|[SeasonalExponentialSmoothingOptimized](../models.html#seasonalexponentialsmoothingoptimized)|βœ…|||||\n", - "|[Holt](../models.html#holt)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", - "|[HoltWinters](../models.html#holtwinters)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", + "|[SimpleExponentialSmoothing](https://nixtla.github.io/statsforecast/src/core/models.html#simpleexponentialsmoothing)|βœ…|||||\n", + "|[SimpleExponentialSmoothingOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#simpleexponentialsmoothingoptimized)|βœ…|||||\n", + "|[SeasonalExponentialSmoothing](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalexponentialsmoothing)|βœ…|||||\n", + "|[SeasonalExponentialSmoothingOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#seasonalexponentialsmoothingoptimized)|βœ…|||||\n", + "|[Holt](https://nixtla.github.io/statsforecast/src/core/models.html#holt)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", + "|[HoltWinters](https://nixtla.github.io/statsforecast/src/core/models.html#holtwinters)|βœ…|βœ…|βœ…|βœ…|βœ…|\n", ": {tbl-colwidths=\"[75,25]\"}\n", "\n", "\n", "\n", "\n", - "### Sparse of Inttermitent\n", + "### Sparse or Inttermitent\n", "Suited for series with very few non-zero observations\n", "\n", "|Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values |\n", "|:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:|\n", - "|[ADIDA](../models.html#adida)|βœ…|||||\n", - "|[CrostonClassic](../models.html#crostonclassic)|βœ…|||||\n", - "|[CrostonOptimized](../models.html#crostonoptimized)|βœ…|||||\n", - "|[CrostonSBA](../models.html#crostonsba)|βœ…|||||\n", - "|[IMAPA](../models.html#imapa)|βœ…|||||\n", - "|[TSB](../models.html#tsb)|βœ…|||||\n", + "|[ADIDA](https://nixtla.github.io/statsforecast/src/core/models.html#adida)|βœ…|||||\n", + "|[CrostonClassic](https://nixtla.github.io/statsforecast/src/core/models.html#crostonclassic)|βœ…|||||\n", + "|[CrostonOptimized](https://nixtla.github.io/statsforecast/src/core/models.html#crostonoptimized)|βœ…|||||\n", + "|[CrostonSBA](https://nixtla.github.io/statsforecast/src/core/models.html#crostonsba)|βœ…|||||\n", + "|[IMAPA](https://nixtla.github.io/statsforecast/src/core/models.html#imapa)|βœ…|||||\n", + "|[TSB](https://nixtla.github.io/statsforecast/src/core/models.html#tsb)|βœ…|||||\n", ": {tbl-colwidths=\"[75,25]\"}\n", "\n" ] diff --git a/nbs/src/adapters.prophet.ipynb b/nbs/src/adapters.prophet.ipynb index 4d1e617ae..07208823b 100644 --- a/nbs/src/adapters.prophet.ipynb +++ b/nbs/src/adapters.prophet.ipynb @@ -180,8 +180,6 @@ " allowmean=False,\n", " blambda=None,\n", " biasadj=False,\n", - " parallel=False,\n", - " num_cores=2,\n", " period=1):\n", " Prophet.__init__(self,\n", " growth,\n", @@ -230,8 +228,6 @@ " allowmean=allowmean,\n", " blambda=blambda,\n", " biasadj=biasadj,\n", - " parallel=parallel,\n", - " num_cores=num_cores,\n", " period=period)\n", " \n", " def fit(self, df, disable_seasonal_features=True, **kwargs):\n", diff --git a/nbs/src/arima.ipynb b/nbs/src/arima.ipynb index 681a65851..8fc44a81b 100644 --- a/nbs/src/arima.ipynb +++ b/nbs/src/arima.ipynb @@ -1872,8 +1872,6 @@ " offset=None,\n", " allow_drift=True,\n", " allow_mean=True,\n", - " parallel=False,\n", - " num_cores=2,\n", " period=1,\n", " **kwargs\n", "):\n", @@ -1882,24 +1880,21 @@ " allow_mean = allow_mean and (d + D) == 0\n", " #max_K = allow_drift or allow_mean\n", " \n", - " if not parallel:\n", - " best_ic = np.inf\n", - " for i in range(max_p):\n", - " for j in range(max_q):\n", - " for I in range(max_P):\n", - " for J in range(max_Q):\n", - " if i + j + I + J > max_order:\n", - " continue\n", - " fit = myarima(\n", - " x,\n", - " order=(i, d, j),\n", - " seasonal={'order': (I, D, J), 'period': m},\n", - " )\n", - " if fit['ic'] < best_ic:\n", - " best_ic = fit['ic']\n", - " best_fit = fit\n", - " else:\n", - " raise NotImplementedError('parallel=True')\n", + " best_ic = np.inf\n", + " for i in range(max_p + 1):\n", + " for j in range(max_q + 1):\n", + " for I in range(max_P + 1):\n", + " for J in range(max_Q + 1):\n", + " if i + j + I + J > max_order:\n", + " continue\n", + " fit = myarima(\n", + " x,\n", + " order=(i, d, j),\n", + " seasonal={'order': (I, D, J), 'period': m},\n", + " )\n", + " if fit['ic'] < best_ic:\n", + " best_ic = fit['ic']\n", + " best_fit = fit\n", " return best_fit" ] }, @@ -2758,18 +2753,10 @@ " allowmean=True,\n", " blambda=None,\n", " biasadj=False,\n", - " parallel=False,\n", - " num_cores=2,\n", " period=1,\n", "):\n", " if approximation is None:\n", " approximation = len(x) > 150 or period > 12\n", - " if stepwise and parallel:\n", - " warnings.warn(\"Parallel computer is only implemented when stepwise=FALSE, the model will be fit in serial.\")\n", - " parallel = False\n", - " if trace and parallel:\n", - " warnings.warn(\"Tracing model searching in parallel is not supported.\")\n", - " trace = False\n", " if x.ndim > 1:\n", " raise ValueError(\"auto_arima can only handle univariate time series\")\n", " if test_kwargs is None:\n", @@ -2966,8 +2953,6 @@ " offset=offset,\n", " allowdrift=allowdrift,\n", " allowmean=allowmean,\n", - " parallel=parallel,\n", - " num_cores=num_cores,\n", " period=m,\n", " )\n", " bestfit['lambda'] = blambda\n", @@ -3500,15 +3485,6 @@ " a regular back transformation will result in median forecasts. \n", " If biasadj is True, an adjustment will be made to produce\n", " mean forecasts and fitted values.\n", - " parallel: bool (default False)\n", - " If True and stepwise = False, then the specification search \n", - " is done in parallel. \n", - " This can give a significant speedup on multicore machines.\n", - " num_cores: int (default 2)\n", - " Allows the user to specify the amount of parallel processes to be used \n", - " if parallel = True and stepwise = False. \n", - " If None, then the number of logical cores is \n", - " automatically detected and all available cores are used.\n", " period: int (default 1)\n", " Number of observations per unit of time.\n", " For example 24 for Hourly data.\n", @@ -3554,8 +3530,6 @@ " allowmean: bool = True,\n", " blambda: Optional[float] = None,\n", " biasadj: bool = False,\n", - " parallel: bool = False,\n", - " num_cores: int = 2,\n", " period: int = 1\n", " ):\n", " self.d=d\n", @@ -3588,8 +3562,6 @@ " self.allowmean=allowmean\n", " self.blambda=blambda\n", " self.biasadj=biasadj\n", - " self.parallel=parallel\n", - " self.num_cores=num_cores\n", " self.period=period\n", " \n", " def fit(self, y: np.ndarray, X: Optional[np.ndarray] = None):\n", @@ -3638,8 +3610,6 @@ " allowmean=self.allowmean,\n", " blambda=self.blambda,\n", " biasadj=self.biasadj,\n", - " parallel=self.parallel,\n", - " num_cores=self.num_cores,\n", " period=self.period\n", " )\n", " self.model_ = ARIMASummary(model_)\n", diff --git a/nbs/src/core/core.ipynb b/nbs/src/core/core.ipynb index b364ab81d..937781e5e 100644 --- a/nbs/src/core/core.ipynb +++ b/nbs/src/core/core.ipynb @@ -79,7 +79,7 @@ "from typing import Any, List, Optional, Union, Dict\n", "import pkg_resources\n", "\n", - "import fugue.api as fa\n", + "from fugue.execution.factory import make_execution_engine\n", "import matplotlib.pyplot as plt\n", "import matplotlib.colors as cm \n", "import numpy as np\n", @@ -182,7 +182,7 @@ " \n", " def _get_cols(self, models, attr, h, X, level=tuple()):\n", " n_models = len(models)\n", - " cuts = np.full(n_models + 1, fill_value=np.nan, dtype=np.int32)\n", + " cuts = np.full(n_models + 1, fill_value=0, dtype=np.int32)\n", " has_level_models = np.full(n_models, fill_value=False, dtype=bool) \n", " cuts[0] = 0\n", " for i_model, model in enumerate(models):\n", @@ -531,7 +531,8 @@ " pass\n", " \n", " def __repr__(self):\n", - " return 'NullModel'\n", + " return \"NullModel\"\n", + "\n", "#test fallback model\n", "fcst_f = ga.forecast(models=[NullModel(), NullModel()], fallback_model=Naive(), h=2, fitted=True)\n", "test_eq(fcst_f['forecasts'], fcsts_fp)\n", @@ -870,9 +871,12 @@ " self._partial_val_df(self.dataframe)\n", "\n", " # datetime check\n", - " dt_arr = self.dataframe['ds'].to_numpy()\n", + " dt_arr = self.dataframe[\"ds\"].to_numpy()\n", " processed_dt_arr = self._check_datetime(dt_arr)\n", - " self.dataframe = self.dataframe.with_columns(pl.from_numpy(processed_dt_arr, schema=['ds']))\n", + " if type(dt_arr) != type(processed_dt_arr):\n", + " self.dataframe = self.dataframe.with_columns(\n", + " pl.from_numpy(processed_dt_arr.to_numpy(), schema=[\"ds\"])\n", + " )\n", "\n", " sample_index_df = self.dataframe[self.non_value_columns]\n", " sorted_index_df = sample_index_df.sort(self.non_value_columns)\n", @@ -1252,7 +1256,7 @@ " List of instantiated objects models.StatsForecast.\n", " freq : str\n", " Frequency of the data.\n", - " See [panda's available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).\n", + " See [pandas' available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).\n", " n_jobs : int (default=1)\n", " Number of jobs used in the parallel processing, use -1 for all cores.\n", " df : pandas.DataFrame or pl.DataFrame, optional (default=None)\n", @@ -1268,6 +1272,7 @@ " \n", " # TODO @fede: needed for residuals, think about it later\n", " self.models = models\n", + " self._validate_model_names()\n", " self.freq = pd.tseries.frequencies.to_offset(freq)\n", " self.n_jobs = n_jobs\n", " self.fallback_model = fallback_model\n", @@ -1275,6 +1280,13 @@ " self.n_jobs == 1\n", " self._prepare_fit(df=df, sort_df=sort_df)\n", "\n", + " def _validate_model_names(self):\n", + " # Some test models don't have alias\n", + " names = [getattr(model, 'alias', lambda: None) for model in self.models]\n", + " names = [x for x in names if x is not None]\n", + " if len(names) != len(set(names)):\n", + " raise ValueError('Model names must be unique. You can use `alias` to set a unique name for each model.')\n", + "\n", " def _prepare_fit(self, df, sort_df):\n", " if df is not None:\n", " df_process = DataFrameProcessing(df, sort_df)\n", @@ -1290,8 +1302,9 @@ " \n", " def _set_prediction_intervals(self, prediction_intervals):\n", " for model in self.models:\n", - " if hasattr(model, 'prediction_intervals'):\n", - " setattr(model, 'prediction_intervals', prediction_intervals)\n", + " interval = getattr(model, \"prediction_intervals\", None)\n", + " if interval is None:\n", + " setattr(model, \"prediction_intervals\", prediction_intervals)\n", " \n", " def fit(\n", " self,\n", @@ -1357,6 +1370,9 @@ " \n", " def _parse_X_level(self, h, X, level):\n", " if X is not None:\n", + " if isinstance(X, pd.DataFrame):\n", + " if X.index.name != \"unique_id\":\n", + " X = X.set_index(\"unique_id\")\n", " expected_shape_rows = h * len(self.ga)\n", " ga_shape = self.ga.data.shape[1]\n", " # Polars doesn't have index, hence, extra \"column\"\n", @@ -2169,19 +2185,19 @@ " prediction_intervals=prediction_intervals,\n", " )\n", " assert df is not None\n", - " with fa.engine_context(infer_by=[df]) as e:\n", - " backend = make_backend(e)\n", - " return backend.forecast(\n", - " df=df,\n", - " models=self.models,\n", - " freq=self.freq,\n", - " fallback_model=self.fallback_model,\n", - " h=h,\n", - " X_df=X_df,\n", - " level=level,\n", - " fitted=fitted,\n", - " prediction_intervals=prediction_intervals,\n", - " )\n", + " engine = make_execution_engine(infer_by=[df])\n", + " backend = make_backend(engine)\n", + " return backend.forecast(\n", + " df=df,\n", + " models=self.models,\n", + " freq=self.freq,\n", + " fallback_model=self.fallback_model,\n", + " h=h,\n", + " X_df=X_df,\n", + " level=level,\n", + " fitted=fitted,\n", + " prediction_intervals=prediction_intervals,\n", + " )\n", " \n", " def cross_validation(\n", " self,\n", @@ -2212,23 +2228,23 @@ " prediction_intervals=prediction_intervals,\n", " )\n", " assert df is not None\n", - " with fa.engine_context(infer_by=[df]) as e:\n", - " backend = make_backend(e)\n", - " return backend.cross_validation(\n", - " df=df,\n", - " models=self.models,\n", - " freq=self.freq,\n", - " fallback_model=self.fallback_model,\n", - " h=h,\n", - " n_windows=n_windows,\n", - " step_size=step_size,\n", - " test_size=test_size,\n", - " input_size=input_size,\n", - " level=level,\n", - " refit=refit,\n", - " fitted=fitted,\n", - " prediction_intervals=prediction_intervals,\n", - " )\n", + " engine = make_execution_engine(infer_by=[df])\n", + " backend = make_backend(engine)\n", + " return backend.cross_validation(\n", + " df=df,\n", + " models=self.models,\n", + " freq=self.freq,\n", + " fallback_model=self.fallback_model,\n", + " h=h,\n", + " n_windows=n_windows,\n", + " step_size=step_size,\n", + " test_size=test_size,\n", + " input_size=input_size,\n", + " level=level,\n", + " refit=refit,\n", + " fitted=fitted,\n", + " prediction_intervals=prediction_intervals,\n", + " )\n", "\n", " def _is_native(self, df) -> bool:\n", " engine = try_get_context_execution_engine()\n", @@ -2261,7 +2277,6 @@ " CrostonClassic,\n", " CrostonOptimized,\n", " CrostonSBA,\n", - " ETS,\n", " HistoricAverage,\n", " IMAPA,\n", " Naive,\n", @@ -2294,7 +2309,7 @@ "# You can try other methods from the `models.StatsForecast` collection\n", "# Check them here: https://nixtla.github.io/statsforecast/models.html\n", "models=[AutoARIMA(), Naive(), \n", - " ETS(), AutoARIMA(allowmean=True, alias='MeanAutoARIMA')] \n", + " AutoETS(), AutoARIMA(allowmean=True, alias='MeanAutoARIMA')] \n", "\n", "# Instantiate StatsForecast class\n", "fcst = StatsForecast(df=panel_df,\n", @@ -2323,6 +2338,19 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc392e5e", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test no duplicate names\n", + "test_fail(lambda: StatsForecast(models=[Naive(), Naive()], freq=\"D\"))\n", + "StatsForecast(models=[Naive(), Naive(alias=\"Naive2\")], freq=\"D\")" + ] + }, { "cell_type": "code", "execution_count": null, @@ -2384,7 +2412,7 @@ "#| hide\n", "fcsts_df = fcst.forecast(h=4, fitted=True, level=[90, 80, 30])\n", "fcsts_df.groupby('unique_id').tail(4)\n", - "fcst.plot(panel_df.groupby('unique_id').tail(28), fcsts_df, models=['AutoARIMA', 'ETS'], level=[90, 80])" + "fcst.plot(panel_df.groupby('unique_id').tail(28), fcsts_df, models=['AutoARIMA', 'AutoETS'], level=[90, 80])" ] }, { @@ -2397,7 +2425,7 @@ "#| hide\n", "fcst.plot(fcst.forecast_fitted_values(),\n", " forecasts_df=fcsts_df,\n", - " models=['AutoARIMA', 'ETS'], level=[80], \n", + " models=['AutoARIMA', 'AutoETS'], level=[80], \n", " max_insample_length=20,\n", " plot_anomalies=True)" ] @@ -2530,6 +2558,24 @@ "fig" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "33d2362f", + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test model prediction_interval overrides\n", + "models=[SimpleExponentialSmoothing(alpha=0.1, prediction_intervals=ConformalIntervals(h=24, n_windows=2))]\n", + "fcst = StatsForecast(df=panel_df,\n", + " models=models,\n", + " freq='D', \n", + " n_jobs=1)\n", + "fcst._set_prediction_intervals(None)\n", + "assert models[0].prediction_intervals is not None" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/nbs/src/core/distributed.fugue.ipynb b/nbs/src/core/distributed.fugue.ipynb index 267bcdd8f..016d6f38b 100644 --- a/nbs/src/core/distributed.fugue.ipynb +++ b/nbs/src/core/distributed.fugue.ipynb @@ -52,6 +52,7 @@ "outputs": [], "source": [ "#| export\n", + "import inspect\n", "from typing import Any, Dict, List\n", "\n", "import numpy as np\n", @@ -113,16 +114,16 @@ " [Source code](https://github.com/Nixtla/statsforecast/blob/main/statsforecast/distributed/fugue.py).\n", "\n", " This class uses [Fugue](https://github.com/fugue-project/fugue) backend capable of distributing \n", - " computation on Spark and Dask without any rewrites.\n", + " computation on Spark, Dask and Ray without any rewrites.\n", "\n", " **Parameters:**
\n", - " `engine`: fugue.ExecutionEngine, a selection between spark and dask.
\n", + " `engine`: fugue.ExecutionEngine, a selection between Spark, Dask, and Ray.
\n", " `conf`: fugue.Config, engine configuration.
\n", " `**transform_kwargs`: additional kwargs for Fugue's transform method.
\n", "\n", " **Notes:**
\n", - " A short introduction to Fugue, with examples on how to scale pandas code to scale pandas \n", - " based code to Spark or Dask is available [here](https://fugue-tutorials.readthedocs.io/tutorials/quick_look/ten_minutes.html).\n", + " A short introduction to Fugue, with examples on how to scale pandas code to Spark, Dask or Ray\n", + " is available [here](https://fugue-tutorials.readthedocs.io/tutorials/quick_look/ten_minutes.html).\n", " \"\"\"\n", " def __init__(\n", " self, \n", @@ -153,7 +154,7 @@ "\n", " **Parameters:**
\n", " `df`: pandas.DataFrame, with columns [`unique_id`, `ds`, `y`] and exogenous.
\n", - " `freq`: str, frequency of the data, [panda's available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).
\n", + " `freq`: str, frequency of the data, [pandas available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).
\n", " `models`: List[typing.Any], list of instantiated objects `StatsForecast.models`.
\n", " `fallback_model`: Any, Model to be used if a model fails.
\n", " `X_df`: pandas.DataFrame, with [unique_id, ds] columns and df’s future exogenous.\n", @@ -165,13 +166,14 @@ " \n", " **References:**
\n", " For more information check the \n", - " [Fugue's transform](https://fugue-tutorials.readthedocs.io/tutorials/beginner/introduction.html#fugue-transform)\n", + " [Fugue's transform](https://fugue-tutorials.readthedocs.io/tutorials/beginner/transform.html)\n", " tutorial.
\n", " The [core.StatsForecast's forecast](https://nixtla.github.io/statsforecast/core.html#statsforecast.forecast)\n", " method documentation.
\n", - " Or the list of available [StatsForecast's models](https://nixtla.github.io/statsforecast/models.html).\n", + " Or the list of available [StatsForecast's models](https://nixtla.github.io/statsforecast/src/core/models.html).\n", " \"\"\"\n", - " schema = \"*-y+\" + str(self._get_output_schema(models))\n", + " level = kwargs.get(\"level\", [])\n", + " schema = \"*-y+\" + str(self._get_output_schema(models, level))\n", " if X_df is None:\n", " return transform(\n", " df,\n", @@ -185,7 +187,7 @@ " **self._transform_kwargs,\n", " )\n", " else:\n", - " schema = \"unique_id:str,ds:str,\" + str(self._get_output_schema(models))\n", + " schema = \"unique_id:str,ds:str,\" + str(self._get_output_schema(models, level))\n", " return _cotransform(\n", " df,\n", " X_df,\n", @@ -234,7 +236,8 @@ " method documentation.
\n", " [Rob J. Hyndman and George Athanasopoulos (2018). \"Forecasting principles and practice, Temporal Cross-Validation\"](https://otexts.com/fpp3/tscv.html).\n", " \"\"\"\n", - " schema = \"*-y+\" + str(self._get_output_schema(models, mode=\"cv\"))\n", + " level = kwargs.get(\"level\", [])\n", + " schema = \"*-y+\" + str(self._get_output_schema(models, level, mode=\"cv\"))\n", " return transform(\n", " df,\n", " self._cv,\n", @@ -269,9 +272,19 @@ " fallback_model=fallback_model, n_jobs=1)\n", " return model.cross_validation(**kwargs).reset_index()\n", "\n", - " def _get_output_schema(self, models, mode=\"forecast\") -> Schema:\n", - " cols: List[Any]\n", - " cols = [(repr(model), np.float32) for model in models]\n", + " def _get_output_schema(self, models, level=None, mode=\"forecast\") -> Schema:\n", + " cols: List[Any] = []\n", + " if level is None:\n", + " level = []\n", + " for model in models:\n", + " has_levels = (\n", + " \"level\" in inspect.signature(getattr(model, \"forecast\")).parameters\n", + " and len(level) > 0\n", + " )\n", + " cols.append((repr(model), np.float32))\n", + " if has_levels:\n", + " cols.extend([(f\"{repr(model)}-lo-{l}\", np.float32) for l in reversed(level)])\n", + " cols.extend([(f\"{repr(model)}-hi-{l}\", np.float32) for l in level])\n", " if mode == \"cv\":\n", " cols = [(\"cutoff\", \"datetime\"), (\"y\", np.float32)] + cols\n", " return Schema(cols)\n", @@ -282,6 +295,57 @@ " return FugueBackend(obj, **kwargs)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5369129", + "metadata": {}, + "outputs": [], + "source": [ + "from statsforecast.core import StatsForecast\n", + "from statsforecast.models import ( \n", + " AutoARIMA,\n", + " AutoETS,\n", + ")\n", + "from statsforecast.utils import generate_series\n", + "\n", + "n_series = 4\n", + "horizon = 7\n", + "\n", + "series = generate_series(n_series)\n", + "\n", + "sf = StatsForecast(\n", + " models=[AutoETS(season_length=7)],\n", + " freq='D',\n", + ")\n", + "\n", + "sf.cross_validation(df=series, h=horizon, step_size = 24,\n", + " n_windows = 2, level=[90]).head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d84def8", + "metadata": {}, + "outputs": [], + "source": [ + "from pyspark.sql import SparkSession\n", + "\n", + "spark = SparkSession.builder.getOrCreate()\n", + "\n", + "# Make unique_id a column\n", + "series = series.reset_index()\n", + "series['unique_id'] = series['unique_id'].astype(str)\n", + "\n", + "# Convert to Spark\n", + "sdf = spark.createDataFrame(series)\n", + "\n", + "# Returns a Spark DataFrame\n", + "sf.cross_validation(df=sdf, h=horizon, step_size = 24,\n", + " n_windows = 2, level=[90]).show()" + ] + }, { "cell_type": "code", "execution_count": null, @@ -391,14 +455,14 @@ "sf = StatsForecast(models=[Naive()], freq='D', fallback_model=Naive())\n", "dask_fcst = sf.forecast(df=df, h=12).compute()\n", "fcst_stats = sf.forecast(df=df.compute(), h=12)\n", - "test_eq(dask_fcst.sort_values(by=['unique_id', 'ds']).reset_index(drop=True), \n", - " fcst_stats.reset_index())" + "test_eq(dask_fcst.sort_values(by=['unique_id', 'ds']).reset_index(drop=True).astype({\"unique_id\": str}), \n", + " fcst_stats.reset_index().astype({\"unique_id\": str}))" ] }, { "cell_type": "code", "execution_count": null, - "id": "964caa9d-d580-44a7-9115-2522e9b64ee9", + "id": "69b6bbb5", "metadata": {}, "outputs": [], "source": [ @@ -510,14 +574,14 @@ "# Distribute predictions.\n", "sf = StatsForecast(models=[Naive()], freq='D')\n", "fcst_fugue = sf.forecast(df=df, h=12).compute().sort_values(['unique_id', 'ds']).reset_index(drop=True)\n", - "fcst_stats = sf.forecast(df=df.compute(), h=12)\n", - "test_eq(fcst_fugue, fcst_stats.reset_index())\n", + "fcst_stats = sf.forecast(df=df.compute(), h=12).reset_index().astype({\"unique_id\": str})\n", + "test_eq(fcst_fugue, fcst_stats)\n", "\n", "# Distribute cross-validation predictions.\n", "sf = StatsForecast(models=[Naive()], freq='D')\n", "fcst_fugue = sf.cross_validation(df=df, h=12).compute().sort_values(['unique_id', 'ds', 'cutoff']).reset_index(drop=True)\n", - "fcst_stats = sf.cross_validation(df=df.compute(), h=12)\n", - "test_eq(fcst_fugue, fcst_stats.reset_index())\n", + "fcst_stats = sf.cross_validation(df=df.compute(), h=12).reset_index().astype({\"unique_id\": str})\n", + "test_eq(fcst_fugue, fcst_stats)\n", "\n", "# fallback model\n", "class FailNaive:\n", @@ -529,8 +593,8 @@ "#cross validation fallback model\n", "fcst = StatsForecast(models=[FailNaive()], freq='D', fallback_model=Naive())\n", "fcst_fugue = fcst.cross_validation(df=df, h=12).compute().sort_values(['unique_id', 'ds', 'cutoff']).reset_index(drop=True)\n", - "fcst_stats = sf.cross_validation(df=df.compute(), h=12)\n", - "test_eq(fcst_fugue, fcst_stats.reset_index())" + "fcst_stats = sf.cross_validation(df=df.compute(), h=12).reset_index().astype({\"unique_id\": str})\n", + "test_eq(fcst_fugue, fcst_stats)" ] }, { @@ -555,14 +619,14 @@ "# Distribute predictions.\n", "sf = StatsForecast(models=[Naive()], freq='D')\n", "fcst_fugue = sf.forecast(df=df, h=12).to_pandas().sort_values(['unique_id', 'ds']).reset_index(drop=True)\n", - "fcst_stats = sf.forecast(df=df.to_pandas(), h=12)\n", - "test_eq(fcst_fugue, fcst_stats.reset_index())\n", + "fcst_stats = sf.forecast(df=df.to_pandas(), h=12).reset_index().astype({\"unique_id\": str})\n", + "test_eq(fcst_fugue, fcst_stats)\n", "\n", "# Distribute cross-validation predictions.\n", "fcst = StatsForecast(models=[Naive()], freq='D')\n", "fcst_fugue = fcst.cross_validation(df=df, h=12).to_pandas().sort_values(['unique_id', 'ds', 'cutoff']).reset_index(drop=True)\n", - "fcst_stats = sf.cross_validation(df=df.to_pandas(), h=12)\n", - "test_eq(fcst_fugue, fcst_stats.reset_index())\n", + "fcst_stats = sf.cross_validation(df=df.to_pandas(), h=12).reset_index().astype({\"unique_id\": str})\n", + "test_eq(fcst_fugue, fcst_stats)\n", "\n", "# fallback model\n", "class FailNaive:\n", @@ -574,8 +638,8 @@ "#cross validation fallback model\n", "sf = StatsForecast(models=[FailNaive()], freq='D', fallback_model=Naive())\n", "fcst_fugue = sf.cross_validation(df=df, h=12).to_pandas().sort_values(['unique_id', 'ds', 'cutoff']).reset_index(drop=True)\n", - "fcst_stats = sf.cross_validation(df=df.to_pandas(), h=12)\n", - "test_eq(fcst_fugue, fcst_stats.reset_index())" + "fcst_stats = sf.cross_validation(df=df.to_pandas(), h=12).reset_index().astype({\"unique_id\": str})\n", + "test_eq(fcst_fugue, fcst_stats)" ] }, { diff --git a/nbs/src/core/models.ipynb b/nbs/src/core/models.ipynb index 2b9fd9d39..519d7e670 100644 --- a/nbs/src/core/models.ipynb +++ b/nbs/src/core/models.ipynb @@ -280,7 +280,21 @@ " \n", " @property\n", " def _conformal_method(self):\n", - " return _get_conformal_method(self.prediction_intervals.method)" + " return _get_conformal_method(self.prediction_intervals.method)\n", + "\n", + " def _store_cs(self, y, X):\n", + " if self.prediction_intervals is not None:\n", + " self._cs = self._conformity_scores(y, X)\n", + "\n", + " def _add_conformal_intervals(self, fcst, y, X, level):\n", + " if self.prediction_intervals is not None and level is not None:\n", + " cs = self._conformity_scores(y, X) if y is not None else self._cs\n", + " res = self._conformal_method(fcst=fcst, cs=cs, level=level)\n", + " return res\n", + " return fcst\n", + "\n", + " def _add_predict_conformal_intervals(self, fcst, level):\n", + " return self._add_conformal_intervals(fcst=fcst, y=None, X=None, level=level)" ] }, { @@ -428,10 +442,6 @@ " Box-Cox transformation parameter.\n", " biasadj : bool \n", " Use adjusted back-transformed mean Box-Cox.\n", - " parallel : bool\n", - " If True and stepwise=False, then parallel search.\n", - " num_cores : int\n", - " Amount of parallel processes to be used if parallel=True.\n", " season_length : int \n", " Number of observations per unit of time. Ex: 24 Hourly data.\n", " alias : str \n", @@ -473,8 +483,6 @@ " allowmean: bool = False,\n", " blambda: Optional[float] = None,\n", " biasadj: bool = False,\n", - " parallel: bool = False,\n", - " num_cores: int = 2,\n", " season_length: int = 1,\n", " alias: str = 'AutoARIMA',\n", " prediction_intervals: Optional[ConformalIntervals] = None,\n", @@ -509,8 +517,6 @@ " self.allowmean=allowmean\n", " self.blambda=blambda\n", " self.biasadj=biasadj\n", - " self.parallel=parallel\n", - " self.num_cores=num_cores\n", " self.season_length=season_length\n", " self.alias = alias\n", " self.prediction_intervals = prediction_intervals\n", @@ -574,13 +580,10 @@ " allowmean=self.allowmean,\n", " blambda=self.blambda,\n", " biasadj=self.biasadj,\n", - " parallel=self.parallel,\n", - " num_cores=self.num_cores,\n", " period=self.season_length\n", " )\n", " \n", - " if self.prediction_intervals is not None:\n", - " self._cs = self._conformity_scores(y=y, X=X)\n", + " self._store_cs(y=y, X=X)\n", " return self\n", " \n", " def predict(\n", @@ -612,15 +615,16 @@ " return res\n", " level = sorted(level)\n", " if self.prediction_intervals is not None:\n", - " res = self._conformal_method(fcst=res, cs=self._cs, level=level)\n", - " return res\n", - " return {\n", - " 'mean': mean,\n", - " **{f'lo-{l}': fcst['lower'][f'{l}%'] for l in reversed(level)},\n", - " **{f'hi-{l}': fcst['upper'][f'{l}%'] for l in level},\n", - " }\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", + " res = {\n", + " 'mean': mean,\n", + " **{f'lo-{l}': fcst['lower'][f'{l}%'] for l in reversed(level)},\n", + " **{f'hi-{l}': fcst['upper'][f'{l}%'] for l in level},\n", + " }\n", + " return res\n", " \n", - " def predict_in_sample(self, level: Optional[Tuple[int]] = None):\n", + " def predict_in_sample(self, level: Optional[List[int]] = None):\n", " \"\"\"Access fitted AutoArima insample predictions.\n", "\n", " Parameters\n", @@ -709,8 +713,6 @@ " allowmean=self.allowmean,\n", " blambda=self.blambda,\n", " biasadj=self.biasadj,\n", - " parallel=self.parallel,\n", - " num_cores=self.num_cores,\n", " period=self.season_length\n", " )\n", " fcst = forecast_arima(mod, h, xreg=X_future, level=level)\n", @@ -720,8 +722,7 @@ " if level is not None:\n", " level = sorted(level)\n", " if self.prediction_intervals is not None:\n", - " cs = self._conformity_scores(y=y, X=X)\n", - " res = self._conformal_method(fcst=res, cs=cs, level=level)\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", " else:\n", " res = {\n", " **res,\n", @@ -776,8 +777,7 @@ " if level is not None:\n", " level = sorted(level)\n", " if self.prediction_intervals is not None:\n", - " cs = self._conformity_scores(y=y, X=X)\n", - " res = self._conformal_method(fcst=res, cs=cs, level=level)\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", " else:\n", " res = {\n", " **res,\n", @@ -880,9 +880,13 @@ "source": [ "#| hide\n", "# test conformal prediction\n", - "arima_c = AutoARIMA(season_length=12, prediction_intervals=ConformalIntervals(h=13, n_windows=5)) \n", + "arima_c = AutoARIMA(season_length=12, prediction_intervals=ConformalIntervals(h=13, n_windows=2)) \n", "test_class(arima_c, x=ap, h=13, level=[90, 80], test_forward=True)\n", - "fcst_arima_c = arima_c.forecast(ap, 13, None, None, (80,95), True)" + "fcst_arima_c = arima_c.forecast(ap, 13, None, None, (80,95), True)\n", + "test_eq(\n", + " fcst_arima_c[\"mean\"],\n", + " fcst_arima[\"mean\"],\n", + ")" ] }, { @@ -1041,6 +1045,10 @@ " A parameter that 'dampens' the trend. \n", " alias : str \n", " Custom name of the model.\n", + " prediction_intervals : Optional[ConformalIntervals],\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\"\n", " def __init__(\n", " self, \n", @@ -1048,11 +1056,13 @@ " model: str = 'ZZZ',\n", " damped: Optional[bool] = None,\n", " alias: str = 'AutoETS',\n", + " prediction_intervals: Optional[ConformalIntervals] = None,\n", " ):\n", " self.season_length = season_length\n", " self.model = model\n", " self.damped = damped\n", " self.alias = alias\n", + " self.prediction_intervals = prediction_intervals\n", " \n", " def __repr__(self):\n", " return self.alias\n", @@ -1072,7 +1082,7 @@ " y : numpy.array \n", " Clean time series of shape (t, ). \n", " X : array-like \n", - " Optional exogenpus of shape (t, n_x). \n", + " Optional exogenous of shape (t, n_x). \n", "\n", " Returns\n", " -------\n", @@ -1081,6 +1091,7 @@ " \"\"\"\n", " self.model_ = ets_f(y, m=self.season_length, model=self.model, damped=self.damped)\n", " self.model_['actual_residuals'] = y - self.model_['fitted']\n", + " self._store_cs(y=y, X=X)\n", " return self\n", " \n", " def predict(\n", @@ -1106,17 +1117,21 @@ " Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n", " \"\"\"\n", " fcst = forecast_ets(self.model_, h=h, level=level)\n", - " mean = fcst['mean']\n", + " res = {'mean': fcst['mean']}\n", " if level is None:\n", - " return {'mean': mean}\n", + " return res\n", " level = sorted(level)\n", - " return {\n", - " 'mean': mean,\n", - " **{f'lo-{l}': fcst[f'lo-{l}'] for l in reversed(level)},\n", - " **{f'hi-{l}': fcst[f'hi-{l}'] for l in level},\n", - " }\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", + " res = {\n", + " **res,\n", + " **{f\"lo-{l}\": fcst[f\"lo-{l}\"] for l in reversed(level)},\n", + " **{f\"hi-{l}\": fcst[f\"hi-{l}\"] for l in level},\n", + " }\n", + " return res\n", " \n", - " def predict_in_sample(self, level: Optional[Tuple[int]] = None):\n", + " def predict_in_sample(self, level: Optional[List[int]] = None):\n", " \"\"\"Access fitted Exponential Smoothing insample predictions.\n", "\n", " Parameters\n", @@ -1179,11 +1194,14 @@ " res = {key: fcst[key] for key in keys}\n", " if level is not None:\n", " level = sorted(level)\n", - " res = {\n", - " **res,\n", - " **{f'lo-{l}': fcst[f'lo-{l}'] for l in reversed(level)},\n", - " **{f'hi-{l}': fcst[f'hi-{l}'] for l in level},\n", - " }\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", + " else:\n", + " res = {\n", + " **res,\n", + " **{f\"lo-{l}\": fcst[f\"lo-{l}\"] for l in reversed(level)},\n", + " **{f\"hi-{l}\": fcst[f\"hi-{l}\"] for l in level},\n", + " }\n", " if fitted:\n", " # add prediction intervals for fitted values\n", " se = _calculate_sigma(y - mod['fitted'], len(y) - mod['n_params'])\n", @@ -1231,11 +1249,14 @@ " res = {key: fcst[key] for key in keys}\n", " if level is not None:\n", " level = sorted(level)\n", - " res = {\n", - " **res,\n", - " **{f'lo-{l}': fcst[f'lo-{l}'] for l in reversed(level)},\n", - " **{f'hi-{l}': fcst[f'hi-{l}'] for l in level},\n", - " }\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", + " else:\n", + " res = {\n", + " **res,\n", + " **{f\"lo-{l}\": fcst[f\"lo-{l}\"] for l in reversed(level)},\n", + " **{f\"hi-{l}\": fcst[f\"hi-{l}\"] for l in level},\n", + " }\n", " if fitted:\n", " # add prediction intervals for fitted values\n", " se = _calculate_sigma(y - mod['fitted'], len(y) - mod['n_params'])\n", @@ -1297,6 +1318,22 @@ "_plot_insample_pi(fcst_ets)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test conformal prediction\n", + "autoets_c = AutoETS(season_length=12, model='AAA', prediction_intervals=ConformalIntervals(h=13, n_windows=2))\n", + "test_class(autoets_c, x=ap, h=13, level=[90, 80], test_forward=True)\n", + "fcst_ets_c = autoets_c.forecast(ap, 13, None, None, (80,95), True)\n", + "test_eq(fcst_ets_c['mean'],\n", + " fcst_ets['mean'])\n", + "_plot_fcst(fcst_ets_c)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1413,12 +1450,14 @@ " \n", " def __init__(self, season_length: int = 1, model: str = 'ZZZ', \n", " damped: Optional[bool] = None,\n", - " alias: str = 'ETS'):\n", + " alias: str = 'ETS',\n", + " prediction_intervals: Optional[ConformalIntervals] = None):\n", " ETS._warn()\n", " self.season_length = season_length\n", " self.model = model\n", " self.damped = damped\n", " self.alias = alias\n", + " self.prediction_intervals = prediction_intervals\n", " \n", " def __repr__(self):\n", " return self.alias" @@ -1550,8 +1589,7 @@ " \"\"\"\n", " self.model_ = auto_ces(y, m=self.season_length, model=self.model)\n", " self.model_['actual_residuals'] = y - self.model_['fitted']\n", - " if self.prediction_intervals is not None:\n", - " self._cs = self._conformity_scores(y=y, X=X)\n", + " self._store_cs(y=y, X=X)\n", " return self\n", " \n", " def predict(\n", @@ -1577,21 +1615,21 @@ " Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n", " \"\"\"\n", " fcst = forecast_ces(self.model_, h=h, level=level)\n", - " mean = fcst['mean']\n", - " res = {'mean': mean}\n", + " res = {\"mean\": fcst[\"mean\"]}\n", " if level is None: \n", " return res\n", " level = sorted(level)\n", " if self.prediction_intervals is not None:\n", - " res = self._conformal_method(fcst=res, cs=self._cs, level=level)\n", - " return res\n", - " return {\n", - " 'mean': mean,\n", - " **{f'lo-{l}': fcst[f'lo-{l}'] for l in reversed(level)},\n", - " **{f'hi-{l}': fcst[f'hi-{l}'] for l in level},\n", - " }\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", + " res = {\n", + " **res,\n", + " **{f\"lo-{l}\": fcst[f\"lo-{l}\"] for l in reversed(level)},\n", + " **{f\"hi-{l}\": fcst[f\"hi-{l}\"] for l in level},\n", + " }\n", + " return res\n", " \n", - " def predict_in_sample(self, level: Optional[Tuple[int]] = None):\n", + " def predict_in_sample(self, level: Optional[List[int]] = None):\n", " \"\"\"Access fitted Exponential Smoothing insample predictions.\n", "\n", " Parameters\n", @@ -1655,8 +1693,7 @@ " if level is not None: \n", " level = sorted(level)\n", " if self.prediction_intervals is not None:\n", - " cs = self._conformity_scores(y=y, X=X)\n", - " res = self._conformal_method(fcst=res, cs=cs, level=level)\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", " else:\n", " res = {\n", " **res, \n", @@ -1711,8 +1748,7 @@ " if level is not None:\n", " level = sorted(level)\n", " if self.prediction_intervals is not None:\n", - " cs = self._conformity_scores(y=y, X=X)\n", - " res = self._conformal_method(fcst=res, cs=cs, level=level)\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", " else:\n", " res = {\n", " **res,\n", @@ -1746,9 +1782,13 @@ "source": [ "#| hide\n", "# test conformal prediction\n", - "autoces_c = AutoCES(season_length=12, prediction_intervals=ConformalIntervals(h=13, n_windows=5)) \n", + "autoces_c = AutoCES(season_length=12, prediction_intervals=ConformalIntervals(h=13, n_windows=2)) \n", "test_class(autoces_c, x=ap, h=13, level=[90, 80], test_forward=True)\n", - "fcst_ces_c = autoces_c.forecast(ap, 13, None, None, (80,95), True)" + "fcst_ces_c = autoces_c.forecast(ap, 13, None, None, (80,95), True)\n", + "test_eq(\n", + " fcst_ces[\"mean\"],\n", + " fcst_ces_c[\"mean\"]\n", + ")" ] }, { @@ -1992,15 +2032,14 @@ " model=self.model, \n", " decomposition_type=self.decomposition_type)\n", " self.model_['fitted'] = y - self.model_['residuals']\n", - " if self.prediction_intervals is not None:\n", - " self._cs = self._conformity_scores(y=y, X=X)\n", + " self._store_cs(y, X)\n", " return self\n", " \n", " def predict(\n", " self, \n", " h: int,\n", " X: Optional[np.ndarray] = None,\n", - " level: Optional[Tuple[int]] = None,\n", + " level: Optional[List[int]] = None,\n", " ):\n", " \"\"\"Predict with fitted AutoTheta.\n", "\n", @@ -2020,10 +2059,10 @@ " \"\"\"\n", " fcst = forecast_theta(self.model_, h=h, level=level)\n", " if self.prediction_intervals is not None and level is not None:\n", - " fcst = self._conformal_method(fcst=fcst, cs=self._cs, level=level)\n", + " fcst = self._add_predict_conformal_intervals(fcst, level)\n", " return fcst\n", " \n", - " def predict_in_sample(self, level: Optional[Tuple[int]] = None):\n", + " def predict_in_sample(self, level: Optional[List[int]] = None):\n", " \"\"\"Access fitted AutoTheta insample predictions.\n", "\n", " Parameters\n", @@ -2084,14 +2123,14 @@ " decomposition_type=self.decomposition_type\n", " )\n", " res = forecast_theta(mod, h, level=level)\n", - " if self.prediction_intervals is not None and level is not None:\n", - " res = self._conformal_method(fcst=res, cs=self._cs, level=level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", " if fitted:\n", " res['fitted'] = y - mod['residuals']\n", " if level is not None and fitted:\n", - " # add prediction intervals for fitted values\n", - " se = np.std(mod['residuals'][3:], ddof=1)\n", - " res = _add_fitted_pi(res=res, se=se, level=level)\n", + " # add prediction intervals for fitted values\n", + " se = np.std(mod['residuals'][3:], ddof=1)\n", + " res = _add_fitted_pi(res=res, se=se, level=level)\n", " return res\n", " \n", " def forward(\n", @@ -2129,14 +2168,14 @@ " raise Exception('You have to use the `fit` method first')\n", " mod = forward_theta(self.model_, y=y)\n", " res = forecast_theta(mod, h, level=level)\n", - " if self.prediction_intervals is not None and level is not None:\n", - " res = self._conformal_method(fcst=res, cs=self._cs, level=level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", " if fitted:\n", " res['fitted'] = y - mod['residuals']\n", " if level is not None and fitted:\n", - " # add prediction intervals for fitted values\n", - " se = np.std(mod['residuals'][3:], ddof=1)\n", - " res = _add_fitted_pi(res=res, se=se, level=level)\n", + " # add prediction intervals for fitted values\n", + " se = np.std(mod['residuals'][3:], ddof=1)\n", + " res = _add_fitted_pi(res=res, se=se, level=level)\n", " return res" ] }, @@ -2161,19 +2200,9 @@ "source": [ "#| hide\n", "# test conformal prediction\n", - "theta_c = AutoTheta(season_length=12, prediction_intervals=ConformalIntervals(h=13, n_windows=5)) \n", + "theta_c = AutoTheta(season_length=12, prediction_intervals=ConformalIntervals(h=13, n_windows=2)) \n", "test_class(theta_c, x=ap, h=13, level=[90, 80], test_forward=True)\n", - "fcst_theta_c = theta_c.forecast(ap, 13, None, None, (80,95), True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "# test equal means\n", + "fcst_theta_c = theta_c.forecast(ap, 13, None, None, (80,95), True)\n", "test_eq(\n", " fcst_theta_c['mean'],\n", " fcst_theta['mean'],\n", @@ -2197,7 +2226,7 @@ "outputs": [], "source": [ "#| hide\n", - "theta.forward(np.zeros(10), h=12, level=[80, 90], fitted=True)" + "zero_theta = theta.forward(np.zeros(10), h=12, level=[80, 90], fitted=True)" ] }, { @@ -2425,8 +2454,7 @@ " method=self.method,\n", " fixed=self.fixed\n", " )\n", - " if self.prediction_intervals is not None:\n", - " self._cs = self._conformity_scores(y=y, X=X)\n", + " self._store_cs(y=y, X=X)\n", " return self\n", " \n", " def predict(\n", @@ -2458,15 +2486,16 @@ " return res\n", " level = sorted(level)\n", " if self.prediction_intervals is not None:\n", - " res = self._conformal_method(fcst=res, cs=self._cs, level=level)\n", - " return res\n", - " return {\n", - " 'mean': mean,\n", - " **{f'lo-{l}': fcst['lower'][f'{l}%'] for l in reversed(level)},\n", - " **{f'hi-{l}': fcst['upper'][f'{l}%'] for l in level},\n", - " }\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", + " res = {\n", + " \"mean\": mean,\n", + " **{f\"lo-{l}\": fcst[\"lower\"][f\"{l}%\"] for l in reversed(level)},\n", + " **{f\"hi-{l}\": fcst[\"upper\"][f\"{l}%\"] for l in level},\n", + " }\n", + " return res\n", " \n", - " def predict_in_sample(self, level: Optional[Tuple[int]] = None):\n", + " def predict_in_sample(self, level: Optional[List[int]] = None):\n", " \"\"\"Access fitted insample predictions.\n", "\n", " Parameters\n", @@ -2543,8 +2572,7 @@ " if level is not None:\n", " level = sorted(level)\n", " if self.prediction_intervals is not None:\n", - " cs = self._conformity_scores(y=y, X=X)\n", - " res = self._conformal_method(fcst=res, cs=cs, level=level)\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", " else:\n", " res = {\n", " **res,\n", @@ -2599,8 +2627,7 @@ " if level is not None:\n", " level = sorted(level)\n", " if self.prediction_intervals is not None:\n", - " cs = self._conformity_scores(y=y, X=X)\n", - " res = self._conformal_method(fcst=res, cs=cs, level=level)\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", " else:\n", " res = {\n", " **res,\n", @@ -2635,18 +2662,9 @@ "source": [ "#| hide\n", "# test conformal prediction\n", - "simple_arima_c = ARIMA(order=(1, 0, 0), season_length=12, prediction_intervals=ConformalIntervals(h=13, n_windows=5)) \n", + "simple_arima_c = ARIMA(order=(1, 0, 0), season_length=12, prediction_intervals=ConformalIntervals(h=13, n_windows=2)) \n", "test_class(simple_arima_c, x=ap, h=13, level=[90, 80], test_forward=True)\n", - "fcst_simple_arima_c = simple_arima_c.forecast(ap, 13, None, None, (80,95), True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", + "fcst_simple_arima_c = simple_arima_c.forecast(ap, 13, None, None, (80,95), True)\n", "test_eq(\n", " fcst_simple_arima_c['mean'],\n", " fcst_simple_arima['mean'],\n", @@ -2883,17 +2901,9 @@ "source": [ "#| hide\n", "# test conformal prediction\n", - "ar_c = AutoRegressive(lags=[12], fixed={'ar12': 0.9999999}, prediction_intervals=ConformalIntervals(h=13, n_windows=5)) \n", + "ar_c = AutoRegressive(lags=[12], fixed={'ar12': 0.9999999}, prediction_intervals=ConformalIntervals(h=13, n_windows=2)) \n", "test_class(ar_c, x=ap, h=13, level=[90, 80], test_forward=True)\n", - "fcst_ar_c = ar_c.forecast(ap, 13, None, None, (80,95), True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "fcst_ar_c = ar_c.forecast(ap, 13, None, None, (80,95), True)\n", "#| hide\n", "test_eq(\n", " fcst_ar_c['mean'],\n", @@ -3160,14 +3170,21 @@ " Smoothing parameter.\n", " alias : str \n", " Custom name of the model. \n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\"\n", " def __init__(\n", " self, \n", " alpha: float,\n", - " alias: str = 'SES'\n", + " alias: str = 'SES',\n", + " prediction_intervals: Optional[ConformalIntervals] = None,\n", " ):\n", " self.alpha = alpha\n", " self.alias = alias\n", + " self.prediction_intervals = prediction_intervals\n", + " self.only_conformal_intervals = True\n", " \n", " def __repr__(self):\n", " return self.alias\n", @@ -3196,12 +3213,14 @@ " \"\"\"\n", " mod = _ses(y=y, alpha=self.alpha, h=1, fitted=True)\n", " self.model_ = dict(mod)\n", + " self._store_cs(y=y, X=X)\n", " return self\n", " \n", " def predict(\n", " self,\n", " h: int,\n", " X: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " ):\n", " \"\"\"Predict with fitted SimpleExponentialSmoothing.\n", "\n", @@ -3211,6 +3230,8 @@ " Forecast horizon.\n", " X : array-like \n", " Optional insample exogenous of shape (t, n_x). \n", + " level : List[float]\n", + " Confidence levels (0-100) for prediction intervals.\n", "\n", " Returns\n", " -------\n", @@ -3219,6 +3240,13 @@ " \"\"\"\n", " mean = _repeat_val(val=self.model_['mean'][0], h=h)\n", " res = {'mean': mean}\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", + " raise Exception(\"You must pass `prediction_intervals` to \" \"compute them.\")\n", " return res\n", " \n", " def predict_in_sample(self):\n", @@ -3244,6 +3272,7 @@ " h: int,\n", " X: Optional[np.ndarray] = None,\n", " X_future: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " fitted: bool = False,\n", " ):\n", " \"\"\"Memory Efficient SimpleExponentialSmoothing predictions.\n", @@ -3272,8 +3301,16 @@ " forecasts : dict \n", " Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n", " \"\"\"\n", - " out = _ses(y=y, h=h, fitted=fitted, alpha=self.alpha)\n", - " return out" + " res = _ses(y=y, h=h, fitted=fitted, alpha=self.alpha)\n", + " res = dict(res)\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", + " else:\n", + " raise Exception(\"You must pass `prediction_intervals` to \" \"compute them.\")\n", + " return res" ] }, { @@ -3298,6 +3335,24 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test conformal prediction\n", + "ses_c = SimpleExponentialSmoothing(alpha=0.1, prediction_intervals=ConformalIntervals(h=13, n_windows=2))\n", + "test_class(ses_c, x=ap, h=13, level=[90, 80], test_forward=False, skip_insample=True)\n", + "fcst_ses_c = ses_c.forecast(ap, 13, None, None, (80,95), True)\n", + "test_eq(\n", + " fcst_ses_c['mean'][:12],\n", + " fcst_ses['mean']\n", + ")\n", + "_plot_fcst(fcst_ses_c)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -3429,9 +3484,20 @@ " ----------\n", " alias: str \n", " Custom name of the model. \n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\"\n", - " def __init__(self, alias: str = 'SESOpt'):\n", + " def __init__(\n", + " self,\n", + " alias: str = \"SESOpt\",\n", + " prediction_intervals: Optional[ConformalIntervals] = None,\n", + " ):\n", " self.alias = alias\n", + " self.prediction_intervals = prediction_intervals\n", + " self.only_conformal_intervals = True\n", + "\n", " \n", " def __repr__(self):\n", " return self.alias\n", @@ -3460,12 +3526,14 @@ " \"\"\"\n", " mod = _ses_optimized(y=y, h=1, fitted=True)\n", " self.model_ = dict(mod)\n", + " self._store_cs(y=y, X=X)\n", " return self\n", "\n", " def predict(\n", " self,\n", " h: int,\n", " X: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " ):\n", " \"\"\"Predict with fitted SimpleExponentialSmoothingOptimized.\n", "\n", @@ -3475,6 +3543,8 @@ " Forecast horizon.\n", " X : array-like \n", " Optional insample exogenous of shape (t, n_x).\n", + " level : List[float]\n", + " Confidence levels (0-100) for prediction intervals.\n", "\n", " Returns\n", " -------\n", @@ -3483,6 +3553,13 @@ " \"\"\"\n", " mean = _repeat_val(val=self.model_['mean'][0], h=h)\n", " res = {'mean': mean}\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", + " raise Exception(\"You must pass `prediction_intervals` to \" \"compute them.\")\n", " return res\n", "\n", " def predict_in_sample(self):\n", @@ -3507,6 +3584,7 @@ " h: int,\n", " X: Optional[np.ndarray] = None,\n", " X_future: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " fitted: bool = False,\n", " ):\n", " \"\"\"Memory Efficient SimpleExponentialSmoothingOptimized predictions.\n", @@ -3535,8 +3613,16 @@ " forecasts : dict \n", " Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n", " \"\"\"\n", - " out = _ses_optimized(y=y, h=h, fitted=fitted)\n", - " return out" + " res = _ses_optimized(y=y, h=h, fitted=fitted)\n", + " res = dict(res)\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", + " else:\n", + " raise Exception(\"You must pass `prediction_intervals` to compute them.\")\n", + " return res" ] }, { @@ -3547,7 +3633,27 @@ "source": [ "#| hide\n", "ses_op = SimpleExponentialSmoothingOptimized()\n", - "test_class(ses_op, x=ap, h=12)" + "test_class(ses_op, x=ap, h=12)\n", + "ses_op = ses_op.fit(ap)\n", + "fcst_ses_op = ses_op.predict(12)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test conformal prediction\n", + "ses_op_c = SimpleExponentialSmoothingOptimized(prediction_intervals=ConformalIntervals(h=13, n_windows=2))\n", + "test_class(ses_op_c, x=ap, h=13, level=[90, 80], skip_insample=True)\n", + "fcst_ses_op_c = ses_op_c.forecast(ap, 13, None, None, (80,95), True)\n", + "test_eq(\n", + " fcst_ses_op_c['mean'][:12],\n", + " fcst_ses_op['mean']\n", + " )\n", + "_plot_fcst(fcst_ses_op_c)" ] }, { @@ -3689,7 +3795,6 @@ "\n", " **References:**
\n", " [Charles. C. Holt (1957). \"Forecasting seasonals and trends by exponentially weighted moving averages\", ONR Research Memorandum, Carnegie Institute of Technology 52.](https://www.sciencedirect.com/science/article/abs/pii/S0169207003001134).\n", - "\n", " [Peter R. Winters (1960). \"Forecasting sales by exponentially weighted moving averages\". Management Science](https://pubsonline.informs.org/doi/abs/10.1287/mnsc.6.3.324).\n", "\n", " Parameters\n", @@ -3699,17 +3804,24 @@ " season_length : int \n", " Number of observations per unit of time. Ex: 24 Hourly data.\n", " alias : str \n", - " Custom name of the model. \n", + " Custom name of the model.\n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\"\n", " def __init__(\n", " self, \n", " season_length: int,\n", " alpha: float,\n", - " alias: str = 'SeasonalES'\n", + " alias: str = 'SeasonalES',\n", + " prediction_intervals: Optional[ConformalIntervals] = None,\n", " ):\n", " self.season_length = season_length\n", " self.alpha = alpha\n", " self.alias = alias\n", + " self.prediction_intervals = prediction_intervals\n", + " self.only_conformal_intervals = True\n", " \n", " def __repr__(self):\n", " return self.alias\n", @@ -3744,12 +3856,14 @@ " h=self.season_length,\n", " )\n", " self.model_ = dict(mod)\n", + " self._store_cs(y=y, X=X)\n", " return self\n", " \n", " def predict(\n", " self,\n", " h: int,\n", " X: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " ):\n", " \"\"\"Predict with fitted SeasonalExponentialSmoothing.\n", "\n", @@ -3759,6 +3873,8 @@ " Forecast horizon.\n", " X : array-like \n", " Optional insample exogenous of shape (t, n_x). \n", + " level : List[float]\n", + " Confidence levels (0-100) for prediction intervals.\n", "\n", " Returns\n", " -------\n", @@ -3767,6 +3883,13 @@ " \"\"\"\n", " mean = _repeat_val_seas(self.model_['mean'], season_length=self.season_length, h=h)\n", " res = {'mean': mean}\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", + " raise Exception(\"You must pass `prediction_intervals` to compute them.\")\n", " return res\n", " \n", " def predict_in_sample(self):\n", @@ -3791,6 +3914,7 @@ " h: int,\n", " X: Optional[np.ndarray] = None,\n", " X_future: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " fitted: bool = False,\n", " ):\n", " \"\"\"Memory Efficient SeasonalExponentialSmoothing predictions.\n", @@ -3819,12 +3943,18 @@ " forecasts : dict \n", " Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n", " \"\"\"\n", - " out = _seasonal_exponential_smoothing(\n", - " y=y, h=h, fitted=fitted, \n", - " alpha=self.alpha,\n", - " season_length=self.season_length\n", + " res = _seasonal_exponential_smoothing(\n", + " y=y, h=h, fitted=fitted, alpha=self.alpha, season_length=self.season_length\n", " )\n", - " return out" + " res = dict(res)\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", + " else:\n", + " raise Exception(\"You must pass `prediction_intervals` to compute them.\")\n", + " return res" ] }, { @@ -3836,7 +3966,27 @@ "#| hide\n", "seas_es = SeasonalExponentialSmoothing(season_length=12, alpha=1.)\n", "test_class(seas_es, x=ap, h=12)\n", - "test_eq(seas_es.predict_in_sample()['fitted'][-3:], np.array([461 - 54., 390 - 28., 432 - 27.]))" + "test_eq(seas_es.predict_in_sample()['fitted'][-3:], np.array([461 - 54., 390 - 28., 432 - 27.]))\n", + "seas_es = seas_es.fit(ap)\n", + "fcst_seas_es = seas_es.predict(12)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test conformal prediction\n", + "seas_es_c = SeasonalExponentialSmoothing(season_length=12, alpha=1., prediction_intervals=ConformalIntervals(h=13, n_windows=2))\n", + "test_class(seas_es_c, x=ap, h=13, level=[90, 80], test_forward=False, skip_insample=True)\n", + "fcst_seas_es_c = seas_es_c.forecast(ap, 13, None, None, (80,95), True)\n", + "test_eq(\n", + " fcst_seas_es_c['mean'][:12],\n", + " fcst_seas_es['mean']\n", + ")\n", + "_plot_fcst(fcst_seas_es_c)" ] }, { @@ -4005,7 +4155,8 @@ " def __init__(\n", " self, \n", " season_length: int,\n", - " alias: str = 'SeasESOpt'\n", + " alias: str = 'SeasESOpt',\n", + " prediction_intervals: Optional[ConformalIntervals] = None,\n", " ):\n", " \"\"\"SeasonalExponentialSmoothingOptimized model.\n", "\n", @@ -4022,17 +4173,23 @@ "\n", " **References:**
\n", " [Charles. C. Holt (1957). \"Forecasting seasonals and trends by exponentially weighted moving averages\", ONR Research Memorandum, Carnegie Institute of Technology 52.](https://www.sciencedirect.com/science/article/abs/pii/S0169207003001134).\n", - "\n", " [Peter R. Winters (1960). \"Forecasting sales by exponentially weighted moving averages\". Management Science](https://pubsonline.informs.org/doi/abs/10.1287/mnsc.6.3.324).\n", " \n", " Parameters\n", + " ----------\n", " season_length : int \n", " Number of observations per unit of time. Ex: 24 Hourly data.\n", " alias : str \n", " Custom name of the model. \n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\"\n", " self.season_length = season_length\n", " self.alias = alias\n", + " self.prediction_intervals = prediction_intervals\n", + " self.only_conformal_intervals = True\n", "\n", " def __repr__(self):\n", " return self.alias\n", @@ -4066,12 +4223,14 @@ " h=self.season_length,\n", " )\n", " self.model_ = dict(mod)\n", + " self._store_cs(y=y, X=X)\n", " return self\n", " \n", " def predict(\n", " self,\n", " h: int,\n", " X: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " ):\n", " \"\"\"Predict with fitted SeasonalExponentialSmoothingOptimized.\n", "\n", @@ -4081,6 +4240,8 @@ " Forecast horizon.\n", " X : array-like \n", " Optional insample exogenous of shape (t, n_x). \n", + " level : List[float]\n", + " Confidence levels (0-100) for prediction intervals.\n", "\n", " Returns\n", " -------\n", @@ -4089,6 +4250,13 @@ " \"\"\"\n", " mean = _repeat_val_seas(self.model_['mean'], season_length=self.season_length, h=h)\n", " res = {'mean': mean}\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", + " raise Exception(\"You must pass `prediction_intervals` to compute them.\")\n", " return res\n", " \n", " def predict_in_sample(self):\n", @@ -4113,6 +4281,7 @@ " h: int,\n", " X: Optional[np.ndarray] = None,\n", " X_future: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " fitted: bool = False,\n", " ):\n", " \"\"\"Memory Efficient SeasonalExponentialSmoothingOptimized predictions.\n", @@ -4141,11 +4310,19 @@ " forecasts : dict \n", " Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n", " \"\"\"\n", - " out = _seasonal_ses_optimized(\n", + " res = _seasonal_ses_optimized(\n", " y=y, h=h, fitted=fitted, \n", " season_length=self.season_length\n", " )\n", - " return out" + " res = dict(res)\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", + " else:\n", + " raise Exception(\"You must pass `prediction_intervals` to compute them.\")\n", + " return res" ] }, { @@ -4156,7 +4333,8 @@ "source": [ "#| hide\n", "seas_es_opt = SeasonalExponentialSmoothingOptimized(season_length=12)\n", - "test_class(seas_es_opt, x=ap, h=12)" + "test_class(seas_es_opt, x=ap, h=12)\n", + "fcst_seas_es_opt = seas_es_opt.forecast(ap, h=12)" ] }, { @@ -4166,12 +4344,15 @@ "outputs": [], "source": [ "#| hide\n", - "for i in range(1, 13):\n", - " test_close(\n", - " ap[i:][-12:],\n", - " seas_es_opt.forecast(ap[i:], h=12)['mean'],\n", - " eps=0.8\n", - " )" + "# test conformal prediction\n", + "seas_es_opt_c = SeasonalExponentialSmoothingOptimized(season_length=12, prediction_intervals=ConformalIntervals(h=13, n_windows=2)) \n", + "test_class(seas_es_opt_c, x=ap, h=13, level=[90, 80], test_forward=False, skip_insample=True)\n", + "fcst_seas_es_opt_c = seas_es_opt_c.forecast(ap, 13, None, None, (80,95), True)\n", + "test_eq(\n", + " fcst_seas_es_opt_c['mean'][:12],\n", + " fcst_seas_es_opt['mean']\n", + ")\n", + "_plot_fcst(fcst_seas_es_opt_c)" ] }, { @@ -4181,8 +4362,23 @@ "outputs": [], "source": [ "#| hide\n", - "#test alias argument\n", - "test_eq(\n", + "for i in range(1, 13):\n", + " test_close(\n", + " ap[i:][-12:],\n", + " seas_es_opt.forecast(ap[i:], h=12)['mean'],\n", + " eps=0.8\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "#test alias argument\n", + "test_eq(\n", " repr(SeasonalExponentialSmoothingOptimized(season_length=12)),\n", " 'SeasESOpt'\n", ")\n", @@ -4286,20 +4482,28 @@ " The type of error of the ETS model. Can be additive (A) or multiplicative (M). \n", " alias : str \n", " Custom name of the model. \n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\"\n", "\n", " def __init__(\n", " self, \n", " season_length: int = 1, \n", " error_type: str = 'A',\n", - " alias: str = 'Holt'\n", + " alias: str = 'Holt',\n", + " prediction_intervals: Optional[ConformalIntervals] = None,\n", " ): \n", "\n", " self.season_length = season_length\n", " self.error_type = error_type\n", " self.alias = alias\n", + " self.prediction_intervals = prediction_intervals\n", " model = error_type + 'AN'\n", - " super().__init__(season_length, model, alias=alias)\n", + " super().__init__(\n", + " season_length, model, alias=alias, prediction_intervals=prediction_intervals\n", + " )\n", " \n", " def __repr__(self):\n", " return self.alias" @@ -4344,6 +4548,25 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide \n", + "holt_c = Holt(season_length=12, error_type='A', prediction_intervals=ConformalIntervals(h=12, n_windows=2))\n", + "fcast_holt_c = holt_c.forecast(ap, 12, level=[80, 90])\n", + "\n", + "ets_c = AutoETS(season_length=12, model='AAN', prediction_intervals=ConformalIntervals(h=12, n_windows=2))\n", + "fcast_ets_c = ets_c.forecast(ap, 12, level=[80, 90])\n", + "\n", + "np.testing.assert_equal(\n", + " fcast_holt_c, \n", + " fcast_ets_c,\n", + ")" + ] + }, { "cell_type": "code", "execution_count": null, @@ -4465,19 +4688,26 @@ " The type of error of the ETS model. Can be additive (A) or multiplicative (M).\n", " alias : str \n", " Custom name of the model. \n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\"\n", " \n", " def __init__(\n", " self, \n", " season_length: int = 1, # season length\n", " error_type: str = 'A', # error type\n", - " alias: str = 'HoltWinters'\n", + " alias: str = 'HoltWinters',\n", + " prediction_intervals: Optional[ConformalIntervals] = None,\n", " ): \n", " self.season_length = season_length\n", " self.error_type = error_type\n", " self.alias = alias\n", " model = error_type + 'A' + error_type\n", - " super().__init__(season_length, model, alias=alias)\n", + " super().__init__(\n", + " season_length, model, alias=alias, prediction_intervals=prediction_intervals\n", + " )\n", " \n", " def __repr__(self):\n", " return self.alias" @@ -4502,6 +4732,25 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide \n", + "hw_c = HoltWinters(season_length=12, error_type='A', prediction_intervals=ConformalIntervals(h=12, n_windows=2))\n", + "fcast_hw_c = hw_c.forecast(ap, 12, level=[80, 90])\n", + "\n", + "ets_c = AutoETS(season_length=12, model='AAA', prediction_intervals=ConformalIntervals(h=12, n_windows=2))\n", + "fcast_ets_c = ets_c.forecast(ap, 12, level=[80, 90])\n", + "\n", + "np.testing.assert_equal(\n", + " fcast_hw_c, \n", + " fcast_ets_c,\n", + ")" + ] + }, { "cell_type": "code", "execution_count": null, @@ -4658,7 +4907,7 @@ "#| export\n", "class HistoricAverage(_TS):\n", "\n", - " def __init__(self, alias: str = 'HistoricAverage'):\n", + " def __init__(self, alias: str = 'HistoricAverage', prediction_intervals: Optional[ConformalIntervals] = None,):\n", " \"\"\"HistoricAverage model.\n", "\n", " Also known as mean method. Uses a simple average of all past observations. \n", @@ -4672,8 +4921,13 @@ " ----------\n", " alias: str\n", " Custom name of the model. \n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\"\n", " self.alias = alias\n", + " self.prediction_intervals = prediction_intervals\n", " \n", " def __repr__(self):\n", " return self.alias\n", @@ -4705,13 +4959,14 @@ " mod['sigma'] = _calculate_sigma(residuals, len(residuals) - 1)\n", " mod['n'] = len(y)\n", " self.model_ = mod\n", + " self._store_cs(y=y, X=X)\n", " return self\n", " \n", " def predict(\n", " self, \n", " h: int,\n", " X: Optional[np.ndarray] = None,\n", - " level: Optional[Tuple[int]] = None,\n", + " level: Optional[List[int]] = None,\n", " ):\n", " \"\"\"Predict with fitted HistoricAverage.\n", "\n", @@ -4724,7 +4979,6 @@ " level : List[float]\n", " Confidence levels (0-100) for prediction intervals. \n", " \n", - "\n", " Returns\n", " -------\n", " forecasts : dict\n", @@ -4733,7 +4987,12 @@ " mean = _repeat_val(val=self.model_['mean'][0], h=h)\n", " res = {'mean': mean}\n", " \n", - " if level is not None: \n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", " sigma = self.model_['sigma']\n", " sigmah = sigma * np.sqrt(1 + (1 / self.model_['n']))\n", " pred_int = _calculate_intervals(res, level, h, sigmah)\n", @@ -4741,7 +5000,7 @@ " \n", " return res\n", " \n", - " def predict_in_sample(self, level: Optional[Tuple[int]] = None):\n", + " def predict_in_sample(self, level: Optional[List[int]] = None):\n", " \"\"\"Access fitted HistoricAverage insample predictions.\n", "\n", " Parameters\n", @@ -4767,7 +5026,7 @@ " h: int,\n", " X: Optional[np.ndarray] = None,\n", " X_future: Optional[np.ndarray] = None,\n", - " level: Optional[Tuple[int]] = None,\n", + " level: Optional[List[int]] = None,\n", " fitted: bool = False,\n", " ):\n", " \n", @@ -4802,14 +5061,20 @@ " \n", " if fitted:\n", " res['fitted'] = out['fitted']\n", - " \n", " if level is not None: \n", - " residuals = y - out['fitted']\n", - " sigma = _calculate_sigma(residuals, len(residuals) - 1)\n", - " sigmah = sigma * np.sqrt(1 + (1 / len(y)))\n", - " pred_int = _calculate_intervals(out, level, h, sigmah)\n", - " res = {**res, **pred_int}\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", + " else:\n", + " residuals = y - out[\"fitted\"]\n", + " sigma = _calculate_sigma(residuals, len(residuals) - 1)\n", + " sigmah = sigma * np.sqrt(1 + (1 / len(y)))\n", + " pred_int = _calculate_intervals(out, level, h, sigmah)\n", + " res = {**res, **pred_int}\n", " if fitted:\n", + " residuals = y - out[\"fitted\"]\n", + " sigma = _calculate_sigma(residuals, len(residuals) - 1)\n", + " sigmah = sigma * np.sqrt(1 + (1 / len(y)))\n", " res = _add_fitted_pi(res=res, se=sigmah, level=level)\n", " \n", " return res" @@ -4853,6 +5118,24 @@ "_plot_insample_pi(fcst_ha)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test conformal prediction\n", + "ha_c = HistoricAverage(prediction_intervals=ConformalIntervals(h=13, n_windows=2))\n", + "test_class(ha_c, x=ap, h=13, level=[90, 80], test_forward=False)\n", + "fcst_ha_c = ha_c.forecast(ap, 13, None, None, (80,95), True)\n", + "test_eq(\n", + " fcst_ha_c['mean'][:12],\n", + " fcst_ha['mean'],\n", + ")\n", + "_plot_fcst(fcst_ha_c)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -4950,10 +5233,11 @@ "#| export\n", "class Naive(_TS):\n", " \n", - " def __init__(self, alias: str = 'Naive'):\n", + " def __init__(self, alias: str = 'Naive', prediction_intervals: Optional[ConformalIntervals] = None):\n", " \"\"\"Naive model.\n", " \n", - " A random walk model, defined as $\\hat{y}_{t+1} = y_t$ for all $t$\n", + " All forecasts have the value of the last observation: \n", + " $\\hat{y}_{t+1} = y_t$ for all $t$\n", " \n", " **References:**
\n", " [Rob J. Hyndman and George Athanasopoulos (2018). \"forecasting principles and practice, Simple Methods\"](https://otexts.com/fpp3/simple-methods.html). \n", @@ -4961,9 +5245,14 @@ " Parameters \n", " ----------\n", " alias: str\n", - " Custom name of the model. \n", + " Custom name of the model.\n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\"\n", " self.alias = alias\n", + " self.prediction_intervals = prediction_intervals\n", " \n", " def __repr__(self):\n", " return self.alias\n", @@ -4995,13 +5284,14 @@ " sigma = _calculate_sigma(residuals, len(residuals) - 1)\n", " mod['sigma'] = sigma\n", " self.model_ = mod\n", + " self._store_cs(y=y, X=X)\n", " return self\n", " \n", " def predict(\n", " self, \n", " h: int, # forecasting horizon \n", " X: Optional[np.ndarray] = None, # exogenous regressors\n", - " level: Optional[Tuple[int]] = None # confidence level\n", + " level: Optional[List[int]] = None # confidence level\n", " ):\n", " \"\"\"Predict with fitted Naive.\n", "\n", @@ -5022,16 +5312,20 @@ " mean = _repeat_val(self.model_['mean'][0], h=h)\n", " res = {'mean': mean}\n", " \n", - " if level is not None: \n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", " steps = np.arange(1,h+1)\n", " sigma = self.model_['sigma']\n", " sigmah = sigma * np.sqrt(steps)\n", " pred_int = _calculate_intervals(res, level, h, sigmah)\n", " res = {**res, **pred_int}\n", - " \n", " return res\n", " \n", - " def predict_in_sample(self, level: Optional[Tuple[int]] = None):\n", + " def predict_in_sample(self, level: Optional[List[int]] = None):\n", " \"\"\"Access fitted Naive insample predictions.\n", "\n", " Parameters\n", @@ -5055,7 +5349,7 @@ " h: int,\n", " X: Optional[np.ndarray] = None,\n", " X_future: Optional[np.ndarray] = None,\n", - " level: Optional[Tuple[int]] = None,\n", + " level: Optional[List[int]] = None,\n", " fitted: bool = False,\n", " ):\n", " \"\"\"Memory Efficient Naive predictions.\n", @@ -5086,20 +5380,23 @@ " \"\"\"\n", " out = _naive(y=y, h=h, fitted=fitted or (level is not None))\n", " res = {'mean': out['mean']}\n", - " \n", " if fitted:\n", " res['fitted'] = out['fitted']\n", - " \n", " if level is not None: \n", - " steps = np.arange(1,h+1)\n", - " residuals = y - out['fitted']\n", - " sigma = _calculate_sigma(residuals, len(residuals) - 1)\n", - " sigmah = sigma * np.sqrt(steps)\n", - " pred_int = _calculate_intervals(out, level, h, sigmah)\n", - " res = {**res, **pred_int}\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", + " else:\n", + " steps = np.arange(1, h + 1)\n", + " residuals = y - out[\"fitted\"]\n", + " sigma = _calculate_sigma(residuals, len(residuals) - 1)\n", + " sigmah = sigma * np.sqrt(steps)\n", + " pred_int = _calculate_intervals(out, level, h, sigmah)\n", + " res = {**res, **pred_int}\n", " if fitted:\n", + " residuals = y - out[\"fitted\"]\n", + " sigma = _calculate_sigma(residuals, len(residuals) - 1)\n", " res = _add_fitted_pi(res=res, se=sigma, level=level)\n", - " \n", " return res" ] }, @@ -5160,6 +5457,24 @@ "_plot_insample_pi(fcst_naive)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test conformal prediction\n", + "naive_c = Naive(prediction_intervals=ConformalIntervals(h=13, n_windows=2)) \n", + "test_class(naive_c, x=ap, h=13, level=[90, 80], test_forward=False)\n", + "fcst_naive_c = naive_c.forecast(ap, 13, None, None, (80,95), True)\n", + "test_eq(\n", + " fcst_naive_c['mean'][:12],\n", + " fcst_naive['mean']\n", + ")\n", + "_plot_fcst(fcst_naive_c)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -5282,7 +5597,7 @@ "#| export\n", "class RandomWalkWithDrift(_TS):\n", " \n", - " def __init__(self, alias: str = 'RWD'):\n", + " def __init__(self, alias: str = 'RWD', prediction_intervals: Optional[ConformalIntervals] = None):\n", " \"\"\"RandomWalkWithDrift model.\n", "\n", " A variation of the naive method allows the forecasts to change over time. \n", @@ -5300,8 +5615,13 @@ " ----------\n", " alias : str \n", " Custom name of the model.\n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\"\n", " self.alias = alias\n", + " self.prediction_intervals = prediction_intervals\n", " \n", " def __repr__(self):\n", " return self.alias\n", @@ -5332,13 +5652,14 @@ " mod['sigma'] = sigma\n", " mod['n'] = len(y)\n", " self.model_ = mod\n", + " self._store_cs(y=y, X=X)\n", " return self\n", " \n", " def predict(\n", " self,\n", " h: int, \n", " X: Optional[np.ndarray] = None,\n", - " level: Optional[Tuple[int]] = None\n", + " level: Optional[List[int]] = None\n", " ):\n", " \"\"\"Predict with fitted RandomWalkWithDrift.\n", "\n", @@ -5360,16 +5681,20 @@ " mean = self.model_['slope'] * (1 + hrange) + self.model_['last_y']\n", " res = {'mean': mean}\n", " \n", - " if level is not None: \n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", " steps = np.arange(1, h + 1)\n", " sigma = self.model_['sigma']\n", " sigmah = sigma * np.sqrt(steps * (1 + steps / (self.model_['n'] - 1)))\n", " pred_int = _calculate_intervals(res, level, h, sigmah)\n", " res = {**res, **pred_int}\n", - " \n", " return res\n", " \n", - " def predict_in_sample(self, level: Optional[Tuple[int]] = None):\n", + " def predict_in_sample(self, level: Optional[List[int]] = None):\n", " \"\"\"Access fitted RandomWalkWithDrift insample predictions.\n", "\n", " Parameters\n", @@ -5393,7 +5718,7 @@ " h: int,\n", " X: Optional[np.ndarray] = None,\n", " X_future: Optional[np.ndarray] = None,\n", - " level: Optional[Tuple[int]] = None,\n", + " level: Optional[List[int]] = None,\n", " fitted: bool = False,\n", " ):\n", " \"\"\"Memory Efficient RandomWalkWithDrift predictions.\n", @@ -5429,13 +5754,19 @@ " res['fitted'] = out['fitted']\n", " \n", " if level is not None: \n", - " steps = np.arange(1, h + 1)\n", - " residuals = y - out['fitted']\n", - " sigma = _calculate_sigma(residuals, len(residuals) - 1)\n", - " sigmah = sigma * np.sqrt(steps * (1 + steps / (len(y) - 1)))\n", - " pred_int = _calculate_intervals(out, level, h, sigmah)\n", - " res = {**res, **pred_int}\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", + " else:\n", + " steps = np.arange(1, h + 1)\n", + " residuals = y - out[\"fitted\"]\n", + " sigma = _calculate_sigma(residuals, len(residuals) - 1)\n", + " sigmah = sigma * np.sqrt(steps * (1 + steps / (len(y) - 1)))\n", + " pred_int = _calculate_intervals(out, level, h, sigmah)\n", + " res = {**res, **pred_int}\n", " if fitted:\n", + " residuals = y - out[\"fitted\"]\n", + " sigma = _calculate_sigma(residuals, len(residuals) - 1)\n", " res = _add_fitted_pi(res=res, se=sigma, level=level)\n", "\n", "\n", @@ -5488,6 +5819,24 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test conformal prediction\n", + "rwd_c = RandomWalkWithDrift(prediction_intervals=ConformalIntervals(h=13, n_windows=2)) \n", + "test_class(rwd_c, x=ap, h=13, level=[90, 80], test_forward=False)\n", + "fcst_rwd_c = rwd_c.forecast(ap, 13, None, None, (80,95), True)\n", + "test_eq(\n", + " fcst_rwd_c['mean'][:12],\n", + " fcst_rwd['mean']\n", + ")\n", + "_plot_fcst(fcst_rwd_c)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -5602,23 +5951,28 @@ "#| export\n", "class SeasonalNaive(_TS):\n", " \n", - " def __init__(self, season_length: int, alias: str = 'SeasonalNaive'):\n", - " self.season_length = season_length\n", - " self.alias = alias\n", + " def __init__(self, season_length: int, alias: str = 'SeasonalNaive', prediction_intervals: Optional[ConformalIntervals] = None):\n", " \"\"\"Seasonal naive model.\n", "\n", - " A method similar to the naive, but uses the last known observation of the same period (e.g. the same month of the previous year) in order to capture seasonal variations. \n", + " A method similar to the naive, but uses the last known observation of the same period (e.g. the same month of the previous year) in order to capture seasonal variations.\n", "\n", " **References:**
\n", - " [Rob J. Hyndman and George Athanasopoulos (2018). \"forecasting principles and practice, Simple Methods\"](https://otexts.com/fpp3/simple-methods.html).\n", + " [Rob J. Hyndman and George Athanasopoulos (2018). \"forecasting principles and practice, Simple Methods\"](https://otexts.com/fpp3/simple-methods.html#seasonal-na%C3%AFve-method).\n", "\n", " Parameters\n", " ----------\n", - " season_length : int \n", + " season_length : int\n", " Number of observations per unit of time. Ex: 24 Hourly data.\n", - " alias : str \n", + " alias : str\n", " Custom name of the model.\n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\"\n", + " self.season_length = season_length\n", + " self.alias = alias\n", + " self.prediction_intervals = prediction_intervals\n", "\n", " def __repr__(self):\n", " return self.alias\n", @@ -5655,6 +6009,7 @@ " mod['sigma'] = _calculate_sigma(residuals, \n", " len(y) - self.season_length)\n", " self.model_ = mod\n", + " self._store_cs(y=y, X=X)\n", " return self\n", " \n", " \n", @@ -5662,7 +6017,7 @@ " self,\n", " h: int, \n", " X: Optional[np.ndarray] = None,\n", - " level: Optional[Tuple[int]] = None, \n", + " level: Optional[List[int]] = None, \n", " ):\n", " \"\"\"Predict with fitted Naive.\n", "\n", @@ -5684,16 +6039,19 @@ " season_length=self.season_length, h=h)\n", " res = {'mean': mean}\n", " \n", - " if level is not None: \n", + " if level is None:\n", + " return res\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", " k = np.floor((h - 1) / self.season_length)\n", " sigma = self.model_['sigma']\n", " sigmah = sigma * np.sqrt(k + 1)\n", " pred_int = _calculate_intervals(res, level, h, sigmah)\n", " res = {**res, **pred_int}\n", - " \n", " return res\n", " \n", - " def predict_in_sample(self, level: Optional[Tuple[int]] = None):\n", + " def predict_in_sample(self, level: Optional[List[int]] = None):\n", " \"\"\"Access fitted SeasonalNaive insample predictions.\n", "\n", " Parameters\n", @@ -5718,7 +6076,7 @@ " h: int,\n", " X: Optional[np.ndarray] = None,\n", " X_future: Optional[np.ndarray] = None,\n", - " level: Optional[Tuple[int]] = None,\n", + " level: Optional[List[int]] = None,\n", " fitted: bool = False,\n", " ):\n", " \"\"\"Memory Efficient SeasonalNaive predictions.\n", @@ -5752,18 +6110,23 @@ " season_length=self.season_length\n", " )\n", " res = {'mean': out['mean']}\n", - " \n", " if fitted:\n", " res['fitted'] = out['fitted']\n", - " \n", " if level is not None: \n", - " k = np.floor((h - 1) / self.season_length)\n", - " residuals = y - out['fitted']\n", - " sigma = _calculate_sigma(residuals, len(y) - self.season_length)\n", - " sigmah = sigma * np.sqrt(k + 1)\n", - " pred_int = _calculate_intervals(out, level, h, sigmah)\n", - " res = {**res, **pred_int}\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", + " else:\n", + " k = np.floor((h - 1) / self.season_length)\n", + " residuals = y - out[\"fitted\"]\n", + " sigma = _calculate_sigma(residuals, len(y) - self.season_length)\n", + " sigmah = sigma * np.sqrt(k + 1)\n", + " pred_int = _calculate_intervals(out, level, h, sigmah)\n", + " res = {**res, **pred_int}\n", " if fitted:\n", + " k = np.floor((h - 1) / self.season_length)\n", + " residuals = y - out[\"fitted\"]\n", + " sigma = _calculate_sigma(residuals, len(y) - self.season_length)\n", " res = _add_fitted_pi(res=res, se=sigma, level=level)\n", " \n", " return res " @@ -5828,6 +6191,24 @@ "_plot_insample_pi(fcst_seas_naive)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test conformal prediction\n", + "seas_naive_c = SeasonalNaive(season_length=12, prediction_intervals=ConformalIntervals(h=13, n_windows=2)) \n", + "test_class(seas_naive_c, x=ap, h=13, level=[90, 80], test_forward=False)\n", + "fcst_seas_naive_c = seas_naive_c.forecast(ap, 13, None, None, (80,95), True)\n", + "test_eq(\n", + " fcst_seas_naive_c['mean'][:12],\n", + " fcst_seas_naive['mean']\n", + ")\n", + "_plot_fcst(fcst_seas_naive_c)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -5951,7 +6332,8 @@ " def __init__(\n", " self, \n", " window_size: int,\n", - " alias: str = 'WindowAverage'\n", + " alias: str = 'WindowAverage',\n", + " prediction_intervals: Optional[ConformalIntervals] = None,\n", " ):\n", " \"\"\"WindowAverage model.\n", "\n", @@ -5969,9 +6351,15 @@ " Size of truncated series on which average is estimated.\n", " alias : str \n", " Custom name of the model. \n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\" \n", " self.window_size = window_size\n", " self.alias = alias\n", + " self.prediction_intervals = prediction_intervals\n", + " self.only_conformal_intervals = True\n", "\n", " def __repr__(self):\n", " return self.alias\n", @@ -6000,12 +6388,14 @@ " \"\"\"\n", " mod = _window_average(y=y, h=1, window_size=self.window_size, fitted=False)\n", " self.model_ = dict(mod)\n", + " self._store_cs(y=y, X=X)\n", " return self\n", " \n", " def predict(\n", " self, \n", " h: int,\n", " X: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " ):\n", " \"\"\"Predict with fitted WindowAverage.\n", "\n", @@ -6013,6 +6403,10 @@ " ----------\n", " h : int \n", " Forecast horizon.\n", + " X : numpy.array\n", + " Optional exogenous of shape (h, n_x).\n", + " level : List[float]\n", + " Confidence levels (0-100) for prediction intervals.\n", "\n", " Returns\n", " -------\n", @@ -6021,6 +6415,13 @@ " \"\"\"\n", " mean = _repeat_val(self.model_['mean'][0], h=h)\n", " res = {'mean': mean}\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", + " raise Exception(\"You must pass `prediction_intervals` to compute them.\")\n", " return res\n", " \n", " def predict_in_sample(self):\n", @@ -6044,6 +6445,7 @@ " h: int,\n", " X: Optional[np.ndarray] = None,\n", " X_future: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " fitted: bool = False,\n", " ):\n", " \"\"\"Memory Efficient WindowAverage predictions.\n", @@ -6072,8 +6474,16 @@ " forecasts : dict \n", " Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n", " \"\"\"\n", - " out = _window_average(y=y, h=h, fitted=fitted, window_size=self.window_size)\n", - " return out" + " res = _window_average(y=y, h=h, fitted=fitted, window_size=self.window_size)\n", + " res = dict(res)\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", + " else:\n", + " raise Exception(\"You must pass `prediction_intervals` to \" \"compute them.\")\n", + " return res" ] }, { @@ -6090,6 +6500,24 @@ "test_close(fcst_w_avg['mean'], np.repeat(ap[-24:].mean(), 12))" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test conformal prediction\n", + "w_avg_c = WindowAverage(window_size=24, prediction_intervals=ConformalIntervals(h=13, n_windows=2)) \n", + "test_class(w_avg_c, x=ap, h=13, level=[90, 80], test_forward=False, skip_insample=True)\n", + "fcst_w_avg_c = w_avg_c.forecast(ap, 13, None, None, (80,95), False)\n", + "test_eq(\n", + " fcst_w_avg_c['mean'][:12],\n", + " fcst_w_avg['mean']\n", + ")\n", + "_plot_fcst(fcst_w_avg_c)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -6219,7 +6647,8 @@ " self, \n", " season_length: int,\n", " window_size: int,\n", - " alias: str = 'SeasWA'\n", + " alias: str = 'SeasWA',\n", + " prediction_intervals: Optional[ConformalIntervals] = None\n", " ):\n", " \"\"\"SeasonalWindowAverage model.\n", "\n", @@ -6236,11 +6665,17 @@ " Number of observations per cycle.\n", " alias : str \n", " Custom name of the model. \n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\" \n", " self.season_length = season_length\n", " self.window_size = window_size\n", " self.alias = alias\n", - "\n", + " self.prediction_intervals = prediction_intervals\n", + " self.only_conformal_intervals = True\n", + " \n", " def __repr__(self):\n", " return self.alias\n", " \n", @@ -6274,12 +6709,14 @@ " window_size=self.window_size,\n", " )\n", " self.model_ = dict(mod)\n", + " self._store_cs(y=y, X=X)\n", " return self\n", " \n", " def predict(\n", " self,\n", " h: int,\n", " X: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " ):\n", " \"\"\"Predict with fitted SeasonalWindowAverage.\n", "\n", @@ -6287,6 +6724,10 @@ " ----------\n", " h : int \n", " Forecast horizon.\n", + " X : array-like\n", + " Optional insample exogenous of shape (t, n_x).\n", + " level : List[float]\n", + " Confidence levels (0-100) for prediction intervals.\n", "\n", " Returns\n", " -------\n", @@ -6296,6 +6737,13 @@ " mean = _repeat_val_seas(season_vals=self.model_['mean'], \n", " season_length=self.season_length, h=h)\n", " res = {'mean': mean}\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", + " raise Exception(\"You must pass `prediction_intervals` to compute them.\")\n", " return res\n", " \n", " def predict_in_sample(self):\n", @@ -6319,6 +6767,7 @@ " h: int,\n", " X: Optional[np.ndarray] = None,\n", " X_future: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " fitted: bool = False,\n", " ):\n", " \"\"\"Memory Efficient SeasonalWindowAverage predictions.\n", @@ -6348,12 +6797,20 @@ " forecasts : dict \n", " Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n", " \"\"\" \n", - " out = _seasonal_window_average(\n", + " res = _seasonal_window_average(\n", " y=y, h=h, fitted=fitted, \n", " season_length=self.season_length,\n", " window_size=self.window_size\n", " )\n", - " return out" + " res = dict(res)\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", + " else:\n", + " raise Exception(\"You must pass `prediction_intervals` to compute them.\")\n", + " return res" ] }, { @@ -6366,8 +6823,26 @@ "seas_w_avg = SeasonalWindowAverage(season_length=12, window_size=1)\n", "test_class(seas_w_avg, x=ap, h=12, skip_insample=True)\n", "seas_w_avg = seas_w_avg.fit(ap)\n", - "fcst_seas_w_avg = w_avg.predict(12)\n", - "test_eq(fcst_w_avg['mean'], fcst_seas_w_avg['mean'])" + "fcst_seas_w_avg = seas_w_avg.predict(12)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test conformal prediction\n", + "seas_w_avg_c = SeasonalWindowAverage(season_length=12, window_size=1, prediction_intervals=ConformalIntervals(h=13, n_windows=2)) \n", + "test_class(seas_w_avg_c, x=ap, h=13, level=[90, 80], test_forward=False, skip_insample=True)\n", + "fcst_seas_w_avg_c = seas_w_avg_c.forecast(ap, 13, None, None, (80,95), False)\n", + "test_eq(\n", + " fcst_seas_w_avg_c['mean'][:12],\n", + " fcst_seas_w_avg['mean']\n", + ")\n", + "fcst_seas_w_avg_c['mean'][:12]\n", + "_plot_fcst(fcst_seas_w_avg_c)" ] }, { @@ -6502,7 +6977,7 @@ "#| export\n", "class ADIDA(_TS):\n", "\n", - " def __init__(self, alias: str = 'ADIDA'):\n", + " def __init__(self, alias: str = 'ADIDA', prediction_intervals: Optional[ConformalIntervals] = None):\n", " \"\"\"ADIDA model.\n", "\n", " Aggregate-Dissagregate Intermittent Demand Approach: Uses temporal aggregation to reduce the \n", @@ -6521,8 +6996,14 @@ " ----------\n", " alias : str \n", " Custom name of the model. \n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\" \n", " self.alias = alias\n", + " self.prediction_intervals = prediction_intervals\n", + " self.only_conformal_intervals = True\n", "\n", " def __repr__(self):\n", " return self.alias\n", @@ -6548,12 +7029,14 @@ " \"\"\" \n", " mod = _adida(y=y, h=1, fitted=False)\n", " self.model_ = dict(mod)\n", + " self._store_cs(y=y, X=X)\n", " return self\n", "\n", " def predict(\n", " self,\n", " h: int,\n", " X: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " ):\n", " \"\"\"Predict with fitted ADIDA.\n", "\n", @@ -6561,6 +7044,10 @@ " ----------\n", " h : int\n", " Forecast horizon.\n", + " X : array-like\n", + " Optional exogenous of shape (h, n_x).\n", + " level : List[float]\n", + " Confidence levels (0-100) for prediction intervals.\n", "\n", " Returns\n", " -------\n", @@ -6569,9 +7056,19 @@ " \"\"\" \n", " mean = _repeat_val(val=self.model_['mean'][0], h=h)\n", " res = {'mean': mean}\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", + " raise Exception(\n", + " \"You have to instantiate the class with `prediction_intervals`\"\n", + " \"to calculate them\"\n", + " )\n", " return res\n", " \n", - " def predict_in_sample(self):\n", + " def predict_in_sample(self,):\n", " \"\"\"Access fitted ADIDA insample predictions.\n", "\n", " Parameters\n", @@ -6592,6 +7089,7 @@ " h: int,\n", " X: Optional[np.ndarray] = None,\n", " X_future: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " fitted: bool = False,\n", " ):\n", " \"\"\"Memory Efficient ADIDA predictions.\n", @@ -6610,7 +7108,9 @@ " Optional insample exogenous of shape (t, n_x). \n", " X_future : array-like \n", " Optional exogenous of shape (h, n_x). \n", - " fitted : bool \n", + " level : List[float]\n", + " Confidence levels (0-100) for prediction intervals.\n", + " fitted : bool \n", " Whether or not to return insample predictions.\n", "\n", " Returns\n", @@ -6618,8 +7118,18 @@ " forecasts : dict \n", " Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n", " \"\"\"\n", - " out = _adida(y=y, h=h, fitted=fitted)\n", - " return out" + " res = _adida(y=y, h=h, fitted=fitted)\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", + " else:\n", + " raise Exception(\n", + " \"You have to instantiate the class with `prediction_intervals`\"\n", + " \"to calculate them\"\n", + " )\n", + " return res" ] }, { @@ -6641,7 +7151,26 @@ "#| hide\n", "adida = ADIDA()\n", "test_class(adida, x=ap, h=12, skip_insample=True)\n", - "test_class(adida, x=deg_ts, h=12, skip_insample=True)" + "test_class(adida, x=deg_ts, h=12, skip_insample=True)\n", + "fcst_adida = adida.forecast(ap, 12)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test conformal prediction\n", + "adida_c = ADIDA(prediction_intervals=ConformalIntervals(h=13, n_windows=2))\n", + "test_class(adida_c, x=ap, h=13, level=[90, 80], skip_insample=True)\n", + "fcst_adida_c = adida_c.forecast(ap, 13, None, None, (80,95), False)\n", + "test_eq(\n", + " fcst_adida_c['mean'][:12],\n", + " fcst_adida['mean'],\n", + ")\n", + "_plot_fcst(fcst_adida_c)" ] }, { @@ -6770,7 +7299,7 @@ "#| export\n", "class CrostonClassic(_TS):\n", " \n", - " def __init__(self, alias: str = 'CrostonClassic'):\n", + " def __init__(self, alias: str = 'CrostonClassic', prediction_intervals: Optional[ConformalIntervals] = None):\n", " \"\"\"CrostonClassic model.\n", "\n", " A method to forecast time series that exhibit intermittent demand.\n", @@ -6788,8 +7317,14 @@ " ----------\n", " alias : str \n", " Custom name of the model.\n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\" \n", " self.alias = alias\n", + " self.prediction_intervals = prediction_intervals\n", + " self.only_conformal_intervals = True\n", "\n", " def __repr__(self):\n", " return self.alias\n", @@ -6815,12 +7350,14 @@ " \"\"\" \n", " mod = _croston_classic(y=y, h=1, fitted=False)\n", " self.model_ = dict(mod)\n", + " self._store_cs(y=y, X=X)\n", " return self\n", " \n", " def predict(\n", " self,\n", " h: int,\n", " X: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " ):\n", " \"\"\"Predict with fitted CrostonClassic.\n", "\n", @@ -6828,6 +7365,10 @@ " ----------\n", " h : int \n", " Forecast horizon.\n", + " X : array-like\n", + " Optional exogenous of shape (h, n_x).\n", + " level : List[float]\n", + " Confidence levels (0-100) for prediction intervals.\n", "\n", " Returns\n", " -------\n", @@ -6836,6 +7377,15 @@ " \"\"\"\n", " mean = _repeat_val(val=self.model_['mean'][0], h=h)\n", " res = {'mean': mean}\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", + " raise Exception(\n", + " \"You have to instantiate the class with `prediction_intervals` to calculate them\"\n", + " )\n", " return res\n", " \n", " def predict_in_sample(self, level):\n", @@ -6859,6 +7409,7 @@ " h: int,\n", " X: Optional[np.ndarray] = None,\n", " X_future: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " fitted: bool = False,\n", " ):\n", " \"\"\"Memory Efficient CrostonClassic predictions.\n", @@ -6877,6 +7428,8 @@ " Optional insample exogenous of shape (t, n_x). \n", " X_future : array-like \n", " Optional exogenous of shape (h, n_x). \n", + " level : List[float]\n", + " Confidence levels (0-100) for prediction intervals.\n", " fitted : bool \n", " Whether or not returns insample predictions.\n", "\n", @@ -6885,8 +7438,17 @@ " forecasts : dict\n", " Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n", " \"\"\" \n", - " out = _croston_classic(y=y, h=h, fitted=fitted)\n", - " return out" + " res = _croston_classic(y=y, h=h, fitted=fitted)\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=dict(res), y=y, X=X, level=level)\n", + " else:\n", + " raise Exception(\n", + " \"You have to instantiate the class with `prediction_intervals` to calculate them\"\n", + " )\n", + " return res\n" ] }, { @@ -6898,7 +7460,26 @@ "#| hide\n", "croston = CrostonClassic()\n", "test_class(croston, x=ap, h=12, skip_insample=True)\n", - "test_class(croston, x=deg_ts, h=12, skip_insample=True)" + "test_class(croston, x=deg_ts, h=12, skip_insample=True)\n", + "fcst_croston = croston.forecast(ap, 12)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test conformal prediction\n", + "croston_c = CrostonClassic(prediction_intervals=ConformalIntervals(h=13, n_windows=2))\n", + "test_class(croston_c, x=ap, h=13, level=[90.0, 80.0], skip_insample=True)\n", + "fcst_croston_c = croston_c.forecast(ap, 13, None, None, (80,95), False)\n", + "test_eq(\n", + " fcst_croston_c['mean'][:12],\n", + " fcst_croston['mean'],\n", + ")\n", + "_plot_fcst(fcst_croston_c)" ] }, { @@ -7026,7 +7607,7 @@ "#| export\n", "class CrostonOptimized(_TS):\n", " \n", - " def __init__(self, alias: str = 'CrostonOptimized'):\n", + " def __init__(self, alias: str = 'CrostonOptimized', prediction_intervals: Optional[ConformalIntervals] = None,):\n", " \"\"\"CrostonOptimized model.\n", "\n", " A method to forecast time series that exhibit intermittent demand.\n", @@ -7045,8 +7626,15 @@ " ----------\n", " alias : str \n", " Custom name of the model.\n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\" \n", " self.alias = alias\n", + " self.prediction_intervals = prediction_intervals\n", + " self.only_conformal_intervals = True\n", + "\n", " \n", " def __repr__(self):\n", " return self.alias\n", @@ -7072,12 +7660,14 @@ " \"\"\" \n", " mod = _croston_optimized(y=y, h=1, fitted=False)\n", " self.model_ = dict(mod)\n", + " self._store_cs(y=y, X=X)\n", " return self\n", " \n", " def predict(\n", " self,\n", " h: int,\n", " X: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " ):\n", " \"\"\"Predict with fitted CrostonOptimized.\n", "\n", @@ -7085,6 +7675,10 @@ " ----------\n", " h : int \n", " Forecast horizon.\n", + " X : array-like\n", + " Optional insample exogenous of shape (t, n_x).\n", + " level : List[float]\n", + " Confidence levels (0-100) for prediction intervals.\n", "\n", " Returns\n", " -------\n", @@ -7093,6 +7687,13 @@ " \"\"\"\n", " mean = _repeat_val(val=self.model_['mean'][0], h=h)\n", " res = {'mean': mean}\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", + " raise Exception(\"You must pass `prediction_intervals` to compute them.\")\n", " return res\n", " \n", " def predict_in_sample(self):\n", @@ -7116,6 +7717,7 @@ " h: int,\n", " X: Optional[np.ndarray] = None,\n", " X_future: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " fitted: bool = False,\n", " ):\n", " \"\"\"Memory Efficient CrostonOptimized predictions.\n", @@ -7142,8 +7744,16 @@ " forecasts : dict \n", " Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n", " \"\"\" \n", - " out = _croston_optimized(y=y, h=h, fitted=fitted)\n", - " return out" + " res = _croston_optimized(y=y, h=h, fitted=fitted)\n", + " res = dict(res)\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", + " else:\n", + " raise Exception(\"You must pass `prediction_intervals` to compute them.\")\n", + " return res" ] }, { @@ -7155,7 +7765,26 @@ "#| hide\n", "croston_op = CrostonOptimized()\n", "test_class(croston_op, x=ap, h=12, skip_insample=True)\n", - "test_class(croston_op, x=deg_ts, h=12, skip_insample=True)" + "test_class(croston_op, x=deg_ts, h=12, skip_insample=True)\n", + "fcst_croston_op = croston_op.forecast(ap, 12)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test conformal prediction\n", + "croston_op_c = CrostonOptimized(prediction_intervals=ConformalIntervals(h=13, n_windows=2))\n", + "test_class(croston_op_c, x=ap, h=13, level=[90, 80], skip_insample=True)\n", + "fcst_croston_op_c = croston_op_c.forecast(ap, 13, None, None, (80,95), False)\n", + "test_eq(\n", + " fcst_croston_op_c['mean'][:12],\n", + " fcst_croston_op['mean'],\n", + ")\n", + "_plot_fcst(fcst_croston_op_c)" ] }, { @@ -7275,7 +7904,7 @@ "#| export\n", "class CrostonSBA(_TS):\n", " \n", - " def __init__(self, alias: str = 'CrostonSBA'):\n", + " def __init__(self, alias: str = 'CrostonSBA', prediction_intervals: Optional[ConformalIntervals] = None,):\n", " \"\"\"CrostonSBA model.\n", "\n", " A method to forecast time series that exhibit intermittent demand.\n", @@ -7294,8 +7923,15 @@ " ----------\n", " alias : str \n", " Custom name of the model.\n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\" \n", " self.alias = alias\n", + " self.prediction_intervals = prediction_intervals\n", + " self.only_conformal_intervals = True\n", + "\n", "\n", " def __repr__(self):\n", " return self.alias\n", @@ -7321,12 +7957,14 @@ " \"\"\"\n", " mod = _croston_sba(y=y, h=1, fitted=False)\n", " self.model_ = dict(mod)\n", + " self._store_cs(y=y, X=X)\n", " return self\n", " \n", " def predict(\n", " self,\n", " h: int,\n", " X: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " ):\n", " \"\"\"Predict with fitted CrostonSBA.\n", "\n", @@ -7334,6 +7972,10 @@ " ----------\n", " h : int \n", " Forecast horizon.\n", + " X : array-like\n", + " Optional exogenous of shape (h, n_x).\n", + " level : List[float]\n", + " Confidence levels (0-100) for prediction intervals.\n", "\n", " Returns\n", " -------\n", @@ -7342,6 +7984,15 @@ " \"\"\"\n", " mean = _repeat_val(val=self.model_['mean'][0], h=h)\n", " res = {'mean': mean}\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", + " raise Exception(\n", + " \"You have to instantiate the class with `prediction_intervals` to calculate them\"\n", + " )\n", " return res\n", " \n", " def predict_in_sample(self):\n", @@ -7365,6 +8016,7 @@ " h: int,\n", " X: Optional[np.ndarray] = None,\n", " X_future: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " fitted: bool = False,\n", " ):\n", " \"\"\"Memory Efficient CrostonSBA predictions.\n", @@ -7383,6 +8035,8 @@ " Optional insample exogenous of shape (t, n_x). \n", " X_future : array-like \n", " Optional exogenous of shape (h, n_x). \n", + " level : List[float]\n", + " Confidence levels (0-100) for prediction intervals.\n", " fitted : bool \n", " Whether or not to return insample predictions.\n", "\n", @@ -7391,8 +8045,18 @@ " forecasts : dict \n", " Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n", " \"\"\" \n", - " out = _croston_sba(y=y, h=h, fitted=fitted)\n", - " return out" + " res = _croston_sba(y=y, h=h, fitted=fitted)\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=dict(res), y=y, X=X, level=level)\n", + " else:\n", + " raise Exception(\n", + " \"You have to instantiate the class with `prediction_intervals`\"\n", + " \"to calculate them\"\n", + " )\n", + " return res" ] }, { @@ -7404,7 +8068,26 @@ "#| hide\n", "croston_sba = CrostonSBA()\n", "test_class(croston_sba, x=ap, h=12, skip_insample=True)\n", - "test_class(croston_sba, x=deg_ts, h=12, skip_insample=True)" + "test_class(croston_sba, x=deg_ts, h=12, skip_insample=True)\n", + "fcst_croston_sba = croston_sba.forecast(ap, 12)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test conformal prediction\n", + "croston_sba_c = CrostonSBA(prediction_intervals=ConformalIntervals(h=13, n_windows=2))\n", + "test_class(croston_sba_c, x=ap, h=13, level=[90, 80], skip_insample=True)\n", + "fcst_croston_sba_c = croston_sba_c.forecast(ap, 13, None, None, (80,95), False)\n", + "test_eq(\n", + " fcst_croston_sba_c['mean'][:12],\n", + " fcst_croston_sba['mean'],\n", + ")\n", + "_plot_fcst(fcst_croston_sba_c)" ] }, { @@ -7535,7 +8218,7 @@ "#| export\n", "class IMAPA(_TS):\n", " \n", - " def __init__(self, alias: str = 'IMAPA'):\n", + " def __init__(self, alias: str = 'IMAPA', prediction_intervals: Optional[ConformalIntervals] = None,):\n", " \"\"\"IMAPA model.\n", "\n", " Intermittent Multiple Aggregation Prediction Algorithm: Similar to ADIDA, but instead of\n", @@ -7550,8 +8233,14 @@ " ----------\n", " alias : str \n", " Custom name of the model. \n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\"\n", " self.alias = alias\n", + " self.prediction_intervals = prediction_intervals\n", + " self.only_conformal_intervals = True\n", "\n", " def __repr__(self):\n", " return self.alias\n", @@ -7577,12 +8266,14 @@ " \"\"\"\n", " mod = _imapa(y=y, h=1, fitted=False)\n", " self.model_ = dict(mod)\n", + " self._store_cs(y=y, X=X)\n", " return self\n", " \n", " def predict(\n", " self,\n", " h: int,\n", " X: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " ):\n", " \"\"\"Predict with fitted IMAPA.\n", "\n", @@ -7595,9 +8286,23 @@ " -------\n", " forecasts : dict \n", " Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n", + " X : array-like\n", + " Optional exogenous of shape (h, n_x).\n", + " level : List[float]\n", + " Confidence levels (0-100) for prediction intervals.\n", " \"\"\"\n", " mean = _repeat_val(val=self.model_['mean'][0], h=h)\n", " res = {'mean': mean}\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", + " raise Exception(\n", + " \"You have to instantiate the class with `prediction_intervals`\"\n", + " \"to calculate them\"\n", + " )\n", " return res\n", " \n", " def predict_in_sample(self):\n", @@ -7621,6 +8326,7 @@ " h: int,\n", " X: Optional[np.ndarray] = None,\n", " X_future: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " fitted: bool = False,\n", " ):\n", " \"\"\"Memory Efficient IMAPA predictions.\n", @@ -7639,6 +8345,8 @@ " Optional insample exogenous of shape (t, n_x). \n", " X_future : array-like \n", " Optional exogenous of shape (h, n_x). \n", + " level : List[float]\n", + " Confidence levels (0-100) for prediction intervals.\n", " fitted : bool \n", " Whether or not to return insample predictions.\n", "\n", @@ -7647,8 +8355,18 @@ " forecasts : dict \n", " Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n", " \"\"\"\n", - " out = _imapa(y=y, h=h, fitted=fitted)\n", - " return out" + " res = _imapa(y=y, h=h, fitted=fitted)\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", + " else:\n", + " raise Exception(\n", + " \"You have to instantiate the class with `prediction_intervals`\"\n", + " \"to calculate them\"\n", + " )\n", + " return res" ] }, { @@ -7660,7 +8378,26 @@ "#| hide\n", "imapa = IMAPA()\n", "test_class(imapa, x=ap, h=12, skip_insample=True)\n", - "test_class(imapa, x=deg_ts, h=12, skip_insample=True)" + "test_class(imapa, x=deg_ts, h=12, skip_insample=True)\n", + "fcst_imapa = imapa.forecast(ap, 12)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test conformal prediction\n", + "imapa_c = IMAPA(prediction_intervals=ConformalIntervals(h=13, n_windows=2))\n", + "test_class(imapa_c, x=ap, h=13, level=[90, 80], skip_insample=True)\n", + "fcst_imapa_c = imapa_c.forecast(ap, 13, None, None, (80,95), False)\n", + "test_eq(\n", + " fcst_imapa_c['mean'][:12],\n", + " fcst_imapa['mean'],\n", + ")\n", + "_plot_fcst(fcst_imapa_c)" ] }, { @@ -7792,7 +8529,8 @@ " self, \n", " alpha_d: float,\n", " alpha_p: float,\n", - " alias: str = 'TSB'\n", + " alias: str = 'TSB',\n", + " prediction_intervals: Optional[ConformalIntervals] = None,\n", " ):\n", " \"\"\"TSB model.\n", "\n", @@ -7824,10 +8562,16 @@ " Smoothing parameter for probability. \n", " alias : str \n", " Custom name of the model. \n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\"\n", " self.alpha_d = alpha_d\n", " self.alpha_p = alpha_p\n", " self.alias = alias\n", + " self.prediction_intervals = prediction_intervals\n", + " self.only_conformal_intervals = True\n", " \n", " def __repr__(self):\n", " return self.alias\n", @@ -7858,12 +8602,14 @@ " alpha_p=self.alpha_p\n", " )\n", " self.model_ = dict(mod)\n", + " self._store_cs(y=y, X=X)\n", " return self\n", " \n", " def predict(\n", " self,\n", " h: int,\n", " X: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " ):\n", " \"\"\"Predict with fitted TSB.\n", "\n", @@ -7871,6 +8617,8 @@ " ----------\n", " h : int \n", " Forecast horizon.\n", + " level : List[float]\n", + " Confidence levels (0-100) for prediction intervals.\n", "\n", " Returns\n", " -------\n", @@ -7879,6 +8627,13 @@ " \"\"\" \n", " mean = _repeat_val(self.model_['mean'][0], h=h)\n", " res = {'mean': mean}\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", + " raise Exception(\"You must pass `prediction_intervals` to \" \"compute them.\")\n", " return res\n", " \n", " def predict_in_sample(self):\n", @@ -7902,6 +8657,7 @@ " h: int,\n", " X: Optional[np.ndarray] = None,\n", " X_future: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", " fitted: bool = False,\n", " ):\n", " \"\"\"Memory Efficient TSB predictions.\n", @@ -7928,13 +8684,21 @@ " forecasts : dict \n", " Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n", " \"\"\"\n", - " out = _tsb(\n", + " res = _tsb(\n", " y=y, h=h, \n", " fitted=fitted, \n", " alpha_d=self.alpha_d, \n", " alpha_p=self.alpha_p\n", " )\n", - " return out" + " res = dict(res)\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", + " else:\n", + " raise Exception(\"You must pass `prediction_intervals` to compute them.\")\n", + " return res" ] }, { @@ -7946,7 +8710,26 @@ "#| hide\n", "tsb = TSB(alpha_d=0.9, alpha_p=0.1)\n", "test_class(tsb, x=ap, h=12, skip_insample=True)\n", - "test_class(tsb, x=deg_ts, h=12, skip_insample=True)" + "test_class(tsb, x=deg_ts, h=12, skip_insample=True)\n", + "fcst_tsb = tsb.forecast(ap, 12)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test conformal prediction\n", + "tsb_c = TSB(alpha_d=0.9, alpha_p=0.1,prediction_intervals=ConformalIntervals(h=13, n_windows=2))\n", + "test_class(tsb_c, x=ap, h=13, level=[90, 80], skip_insample=True)\n", + "fcst_tsb_c = tsb_c.forecast(ap, 13, None, None, (80,95), False)\n", + "test_eq(\n", + " fcst_tsb_c['mean'][:12],\n", + " fcst_tsb['mean'],\n", + ")\n", + "_plot_fcst(fcst_tsb_c)" ] }, { @@ -8091,7 +8874,11 @@ " Extra arguments to pass to [`statsmodels.tsa.seasonal.STL`](https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html#statsmodels.tsa.seasonal.STL).\n", " The `period` and `seasonal` arguments are reserved.\n", " alias : str\n", - " Custom name of the model. \n", + " Custom name of the model.\n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\"\n", " \n", " def __init__(\n", @@ -8100,6 +8887,7 @@ " trend_forecaster = AutoETS(model='ZZN'),\n", " stl_kwargs: Optional[Dict] = None,\n", " alias: str = 'MSTL',\n", + " prediction_intervals: Optional[ConformalIntervals] = None,\n", " ):\n", " \n", " # check ETS model doesnt have seasonality\n", @@ -8119,7 +8907,11 @@ " )\n", " self.season_length = season_length\n", " self.trend_forecaster = trend_forecaster\n", + " self.prediction_intervals = prediction_intervals\n", " self.alias = alias\n", + "\n", + " if self.trend_forecaster.prediction_intervals is None and (self.prediction_intervals is not None):\n", + " self.trend_forecaster.prediction_intervals = prediction_intervals\n", " self.stl_kwargs = dict() if stl_kwargs is None else stl_kwargs\n", " \n", " def __repr__(self):\n", @@ -8153,13 +8945,14 @@ " )\n", " x_sa = self.model_[['trend', 'remainder']].sum(axis=1).values\n", " self.trend_forecaster = self.trend_forecaster.new().fit(y=x_sa, X=X)\n", + " self._store_cs(y=y, X=X)\n", " return self\n", " \n", " def predict(\n", " self,\n", " h: int,\n", " X: Optional[np.ndarray] = None,\n", - " level: Optional[Tuple[int]] = None,\n", + " level: Optional[List[int]] = None,\n", " ):\n", " \"\"\"Predict with fitted MSTL.\n", "\n", @@ -8169,7 +8962,7 @@ " Forecast horizon.\n", " X : array-like \n", " Optional exogenous of shape (h, n_x). \n", - " level : List[floar] \n", + " level : List[float] \n", " Confidence levels (0-100) for prediction intervals.\n", "\n", " Returns\n", @@ -8183,9 +8976,18 @@ " res = self.trend_forecaster.predict(**kwargs)\n", " seas = _predict_mstl_seas(self.model_, h=h, season_length=self.season_length)\n", " res = {key: val + seas for key, val in res.items()}\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.trend_forecaster.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", + " raise Exception(\n", + " \"You have to instantiate either the trend forecaster class or MSTL class with `prediction_intervals` to calculate them\"\n", + " )\n", " return res\n", " \n", - " def predict_in_sample(self, level: Optional[Tuple[int]] = None):\n", + " def predict_in_sample(self, level: Optional[List[int]] = None):\n", " \"\"\"Access fitted MSTL insample predictions.\n", "\n", " Parameters\n", @@ -8265,6 +9067,15 @@ " key: val + (seas_insample if 'fitted' in key else seas_h) \\\n", " for key, val in res.items()\n", " }\n", + " if level is None:\n", + " return res\n", + " level = sorted(level)\n", + " if self.trend_forecaster.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", + " else:\n", + " raise Exception(\n", + " \"You have to instantiate either the trend forecaster class or MSTL class with `prediction_intervals` to calculate them\"\n", + " )\n", " return res\n", " \n", " def forward(\n", @@ -8323,6 +9134,10 @@ " key: val + (seas_insample if 'fitted' in key else seas_h) \\\n", " for key, val in res.items()\n", " }\n", + " if level is not None:\n", + " level = sorted(level)\n", + " if self.trend_forecaster.prediction_intervals is not None:\n", + " res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)\n", " return res" ] }, @@ -8351,10 +9166,65 @@ " )\n", " test_class(mstl_model, x=ap, h=12, \n", " skip_insample=skip_insample,\n", - " level=[80, 90] if not skip_insample else None,\n", + " level=None,\n", " test_forward=test_forward)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# conformal prediction\n", + "# define the prediction interval in the trend_forecaster\n", + "trend_forecasters = [\n", + " AutoARIMA(prediction_intervals=ConformalIntervals(h=13, n_windows=2)), \n", + " AutoCES(prediction_intervals=ConformalIntervals(h=13, n_windows=2)), \n", + "]\n", + "skip_insamples = [False, True]\n", + "test_forwards = [False, True]\n", + "for trend_forecaster, skip_insample, test_forward in zip(trend_forecasters, skip_insamples, test_forwards):\n", + " for stl_kwargs in [None, dict(trend=25)]:\n", + " mstl_model = MSTL(\n", + " season_length=[12, 14], \n", + " trend_forecaster=trend_forecaster,\n", + " stl_kwargs=stl_kwargs,\n", + " )\n", + " test_class(mstl_model, x=ap, h=13, \n", + " skip_insample=skip_insample,\n", + " level=[80, 90] if not skip_insample else None,\n", + " test_forward=test_forward)\n", + " _plot_fcst(mstl_model.forecast(ap, 13, None, None, (80,95), False))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# conformal prediction\n", + "# define prediction_interval in MSTL\n", + "trend_forecasters = [\n", + " AutoCES()\n", + "]\n", + "for stl_kwargs in [None, dict(trend=25)]:\n", + " mstl_model = MSTL(\n", + " season_length=[12, 14], \n", + " trend_forecaster=trend_forecaster,\n", + " stl_kwargs=stl_kwargs,\n", + " prediction_intervals=ConformalIntervals(h=13, n_windows=2)\n", + " )\n", + " test_class(mstl_model, x=ap, h=13, \n", + " skip_insample=False,\n", + " level=[80, 90] if not skip_insample else None,\n", + " test_forward=True)\n", + " _plot_fcst(mstl_model.forecast(ap, 13, None, None, (80,95), False))" + ] + }, { "cell_type": "code", "execution_count": null, @@ -8459,7 +9329,7 @@ "from statsforecast.utils import AirPassengers as ap\n", "\n", "\n", - "mstl_model = MSTL(season_length=[3, 12], trend_forecaster=AutoARIMA())\n", + "mstl_model = MSTL(season_length=[3, 12], trend_forecaster=AutoARIMA(prediction_intervals=ConformalIntervals(h=4, n_windows=2)))\n", "mstl_model = mstl_model.fit(y=ap)\n", "y_hat_dict = mstl_model.predict(h=4, level=[80])\n", "y_hat_dict" @@ -9341,12 +10211,17 @@ " Number of lagged versions of the volatility. \n", " alias : str \n", " Custom name of the model. \n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\"\n", " def __init__(\n", " self, \n", " p: int = 1,\n", " q: int = 1,\n", - " alias: str = 'GARCH'\n", + " alias: str = 'GARCH',\n", + " prediction_intervals: Optional[ConformalIntervals] = None,\n", " ):\n", " self.p = p\n", " self.q = q\n", @@ -9354,6 +10229,7 @@ " self.alias = alias+'('+str(p)+','+str(q)+')'\n", " else: \n", " self.alias = alias+'('+str(p)+')'\n", + " self.prediction_intervals = prediction_intervals\n", " \n", " def __repr__(self):\n", " return self.alias\n", @@ -9377,9 +10253,9 @@ " self : \n", " GARCH model.\n", " \"\"\"\n", - " \n", " self.model_ = garch_model(y, p=self.p, q=self.q)\n", " self.model_['actual_residuals'] = y - self.model_['fitted']\n", + " self._store_cs(y, X)\n", " return self\n", " \n", " def predict(\n", @@ -9394,7 +10270,9 @@ " ----------\n", " h : int \n", " Forecast horizon.\n", - " level : List[float] \n", + " X : array-like\n", + " Optional exogenous of shape (h, n_x).\n", + " level : List[float]\n", " Confidence levels (0-100) for prediction intervals.\n", "\n", " Returns\n", @@ -9404,8 +10282,12 @@ " \"\"\"\n", " fcst = garch_forecast(self.model_, h)\n", " res = {'mean': fcst['mean'], 'sigma2': fcst['sigma2']}\n", - " if level is not None: \n", - " level = sorted(level) \n", + " if level is None: \n", + " return res\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else: \n", " quantiles = _quantiles(level)\n", " lo = res['mean'].reshape(-1, 1) - quantiles * res['sigma2'].reshape(-1, 1)\n", " hi = res['mean'].reshape(-1, 1) + quantiles * res['sigma2'].reshape(-1, 1)\n", @@ -9415,7 +10297,7 @@ " res = {**res, **lo, **hi}\n", " return res\n", " \n", - " def predict_in_sample(self, level: Optional[Tuple[int]] = None):\n", + " def predict_in_sample(self, level: Optional[List[int]] = None):\n", " \"\"\"Access fitted GARCH model predictions.\n", "\n", " Parameters\n", @@ -9473,14 +10355,17 @@ " keys.append('fitted')\n", " res = {key: fcst[key] for key in keys}\n", " if level is not None: \n", - " level = sorted(level) \n", - " quantiles = _quantiles(level)\n", - " lo = res['mean'].reshape(-1, 1) - quantiles * res['sigma2'].reshape(-1, 1)\n", - " hi = res['mean'].reshape(-1, 1) + quantiles * res['sigma2'].reshape(-1, 1)\n", - " lo = lo[:, ::-1]\n", - " lo = {f'lo-{l}': lo[:, i] for i, l in enumerate(reversed(level))}\n", - " hi = {f'hi-{l}': hi[:, i] for i, l in enumerate(level)}\n", - " res = {**res, **lo, **hi}\n", + " level = sorted(level)\n", + " if self.prediction_intervals is not None:\n", + " res = self._add_predict_conformal_intervals(res, level)\n", + " else:\n", + " quantiles = _quantiles(level)\n", + " lo = res['mean'].reshape(-1, 1) - quantiles * res['sigma2'].reshape(-1, 1)\n", + " hi = res['mean'].reshape(-1, 1) + quantiles * res['sigma2'].reshape(-1, 1)\n", + " lo = lo[:, ::-1]\n", + " lo = {f'lo-{l}': lo[:, i] for i, l in enumerate(reversed(level))}\n", + " hi = {f'hi-{l}': hi[:, i] for i, l in enumerate(level)}\n", + " res = {**res, **lo, **hi}\n", " if fitted: \n", " se = _calculate_sigma(y - mod['fitted'], len(y) - 1)\n", " res = _add_fitted_pi(res=res, se=se, level=level)\n", @@ -9514,7 +10399,26 @@ "source": [ "#| hide \n", "garch = GARCH(2,2)\n", - "test_class(garch, x=y, h=12, skip_insample=False, level=[90,80])" + "test_class(garch, x=y, h=12, skip_insample=False, level=[90,80])\n", + "fcst_garch = garch.forecast(ap, 12)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test conformal prediction\n", + "garch_c = GARCH(2,2,prediction_intervals=ConformalIntervals(h=13, n_windows=2)) \n", + "test_class(garch_c, x=ap, h=13, level=[90, 80], test_forward=False)\n", + "fcst_garch_c = garch_c.forecast(ap, 13, None, None, (80,95), True)\n", + "test_eq(\n", + " fcst_garch_c['mean'][:12],\n", + " fcst_garch['mean']\n", + ")\n", + "_plot_fcst(fcst_garch_c)" ] }, { @@ -9649,12 +10553,17 @@ " Number of lagged versions of the series. \n", " alias : str \n", " Custom name of the model. \n", + " prediction_intervals : Optional[ConformalIntervals]\n", + " Information to compute conformal prediction intervals.\n", + " By default, the model will compute the native prediction\n", + " intervals.\n", " \"\"\"\n", " \n", " def __init__(\n", " self, \n", " p: int = 1,\n", - " alias: str = 'ARCH'\n", + " alias: str = 'ARCH',\n", + " prediction_intervals: Optional[ConformalIntervals] = None\n", " ):\n", " self.p = p\n", " self.alias = alias\n", @@ -9672,7 +10581,26 @@ "source": [ "#| hide \n", "arch = ARCH(1)\n", - "test_class(arch, x=y, h=12, skip_insample=False, level=[90,80])" + "test_class(arch, x=y, h=12, skip_insample=False, level=[90,80])\n", + "fcst_arch = arch.forecast(ap, 12)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "# test conformal prediction\n", + "arch_c = ARCH(1,prediction_intervals=ConformalIntervals(h=13, n_windows=2)) \n", + "test_class(arch_c, x=ap, h=13, level=[90, 80], test_forward=False)\n", + "fcst_arch_c = arch_c.forecast(ap, 13, None, None, (80,95), True)\n", + "test_eq(\n", + " fcst_arch_c['mean'][:12],\n", + " fcst_arch['mean']\n", + ")\n", + "_plot_fcst(fcst_arch_c)" ] }, { @@ -9739,6 +10667,531 @@ "show_doc(ARCH.forecast, name='ARCH.forecast', title_level=3)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fallback Models " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ConstantModel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "class ConstantModel(_TS):\n", + " \n", + " def __init__(self, constant: float, alias: str = 'ConstantModel'):\n", + " \"\"\"Constant Model.\n", + " \n", + " Returns Constant values.\n", + " \n", + " Parameters \n", + " ----------\n", + " constant: float\n", + " Custom value to return as forecast.\n", + " alias: str\n", + " Custom name of the model. \n", + " \"\"\"\n", + " self.constant = constant\n", + " self.alias = alias\n", + " \n", + " def __repr__(self):\n", + " return self.alias\n", + " \n", + " def fit(\n", + " self, \n", + " y: np.ndarray,\n", + " X: Optional[np.ndarray] = None,\n", + " ):\n", + " \"\"\"Fit the Constant model.\n", + "\n", + " Fit an Constant Model to a time series (numpy.array) `y`.\n", + "\n", + " Parameters\n", + " ----------\n", + " y : numpy.array\n", + " Clean time series of shape (t, ). \n", + " X : array-like\n", + " Optional exogenous of shape (t, n_x). \n", + "\n", + " Returns\n", + " -------\n", + " self:\n", + " Constant fitted model.\n", + " \"\"\"\n", + " self.n_y = len(y)\n", + " return self\n", + " \n", + " def predict(\n", + " self, \n", + " h: int, # forecasting horizon \n", + " X: Optional[np.ndarray] = None, # exogenous regressors\n", + " level: Optional[List[int]] = None # confidence level\n", + " ):\n", + " \"\"\"Predict with fitted ConstantModel.\n", + "\n", + " Parameters\n", + " ----------\n", + " h : int \n", + " Forecast horizon.\n", + " X : array-like\n", + " Optional exogenous of shape (h, n_x). \n", + " level : List[float] \n", + " Confidence levels (0-100) for prediction intervals. \n", + "\n", + " Returns\n", + " -------\n", + " forecasts : dict\n", + " Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n", + " \"\"\"\n", + " mean = np.full(h, self.constant, dtype=np.float32)\n", + " res = {'mean': mean}\n", + " \n", + " if level is not None: \n", + " for lv in sorted(level):\n", + " res[f'lo-{lv}'] = mean\n", + " res[f'hi-{lv}'] = mean\n", + " \n", + " return res\n", + " \n", + " def predict_in_sample(self, level: Optional[List[int]] = None):\n", + " \"\"\"Access fitted Constant Model insample predictions.\n", + "\n", + " Parameters\n", + " ----------\n", + " level : List[float]\n", + " Confidence levels (0-100) for prediction intervals.\n", + "\n", + " Returns\n", + " -------\n", + " forecasts : dict\n", + " Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n", + " \"\"\"\n", + " fitted = np.full(self.n_y, self.constant, dtype=np.float32)\n", + " res = {'fitted': fitted}\n", + " if level is not None:\n", + " for lv in sorted(level):\n", + " res[f'fitted-lo-{lv}'] = fitted\n", + " res[f'fitted-hi-{lv}'] = fitted\n", + " \n", + " return res\n", + " \n", + " def forecast(\n", + " self, \n", + " y: np.ndarray,\n", + " h: int,\n", + " X: Optional[np.ndarray] = None,\n", + " X_future: Optional[np.ndarray] = None,\n", + " level: Optional[List[int]] = None,\n", + " fitted: bool = False,\n", + " ):\n", + " \"\"\"Memory Efficient Constant Model predictions.\n", + "\n", + " This method avoids memory burden due from object storage.\n", + " It is analogous to `fit_predict` without storing information.\n", + " It assumes you know the forecast horizon in advance.\n", + "\n", + " Parameters\n", + " ----------\n", + " y : numpy.array \n", + " Clean time series of shape (n,). \n", + " h: int\n", + " Forecast horizon.\n", + " X : array-like\n", + " Optional insample exogenous of shape (t, n_x). \n", + " X_future : array-like\n", + " Optional exogenous of shape (h, n_x). \n", + " level : List[float]\n", + " Confidence levels (0-100) for prediction intervals.\n", + " fitted : bool\n", + " Whether or not to return insample predictions.\n", + "\n", + " Returns\n", + " -------\n", + " forecasts : dict\n", + " Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.\n", + " \"\"\"\n", + " mean = np.full(h, self.constant, dtype=np.float32)\n", + " res = {'mean': mean}\n", + " \n", + " if fitted:\n", + " fitted_vals = np.full(self.n_y, self.constant, dtype=np.float32)\n", + " res['fitted'] = fitted_vals\n", + " \n", + " if level is not None: \n", + " for lv in sorted(level):\n", + " res[f'lo-{lv}'] = mean\n", + " res[f'hi-{lv}'] = mean\n", + " if fitted:\n", + " res[f'fitted-lo-{lv}'] = fitted_vals\n", + " res[f'fitted-hi-{lv}'] = fitted_vals\n", + " return res" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "constant_model = ConstantModel(constant=1)\n", + "test_class(constant_model, x=ap, h=12, level=[90, 80])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "constant_model.forecast(ap, 12, level=[90, 80])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "#test alias argument\n", + "test_eq(\n", + " repr(ConstantModel(1)),\n", + " 'ConstantModel'\n", + ")\n", + "test_eq(\n", + " repr(ConstantModel(1, alias='ConstantModel_custom')),\n", + " 'ConstantModel_custom'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_doc(ConstantModel, title_level=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_doc(ConstantModel.forecast, title_level=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_doc(ConstantModel.fit, title_level=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_doc(ConstantModel.predict, title_level=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_doc(ConstantModel.predict_in_sample, title_level=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ConstantModel's usage example\n", + "\n", + "from statsforecast.models import ConstantModel\n", + "from statsforecast.utils import AirPassengers as ap\n", + "\n", + "\n", + "model = ConstantModel(1)\n", + "model = model.fit(y=ap)\n", + "y_hat_dict = model.predict(h=4)\n", + "y_hat_dict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ZeroModel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "class ZeroModel(ConstantModel):\n", + " \n", + " def __init__(self, alias: str = 'ZeroModel'):\n", + " \"\"\"Returns Zero forecasts.\n", + " \n", + " Returns Zero values.\n", + " \n", + " Parameters \n", + " ----------\n", + " alias: str\n", + " Custom name of the model. \n", + " \"\"\"\n", + " super().__init__(constant=0, alias=alias)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "zero_model = ZeroModel()\n", + "test_class(constant_model, x=ap, h=12, level=[90, 80])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "zero_model.forecast(ap, 12, level=[90, 80])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "#test alias argument\n", + "test_eq(\n", + " repr(ZeroModel()),\n", + " 'ZeroModel'\n", + ")\n", + "test_eq(\n", + " repr(ZeroModel(alias='ZeroModel_custom')),\n", + " 'ZeroModel_custom'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_doc(ZeroModel, title_level=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_doc(ZeroModel.forecast, title_level=3, name='ZeroModel.forecast')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_doc(ZeroModel.fit, title_level=3, name='ZeroModel.fit')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_doc(ZeroModel.predict, title_level=3, name='ZeroModel.predict')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_doc(ZeroModel.predict_in_sample, title_level=3, name='ZeroModel.predict_in_sample')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NanModel's usage example\n", + "\n", + "from statsforecast.models import ZeroModel\n", + "from statsforecast.utils import AirPassengers as ap\n", + "\n", + "\n", + "model = ZeroModel()\n", + "model = model.fit(y=ap)\n", + "y_hat_dict = model.predict(h=4)\n", + "y_hat_dict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NaNModel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export\n", + "class NaNModel(ConstantModel):\n", + " \n", + " def __init__(self, alias: str = 'NaNModel'):\n", + " \"\"\"NaN Model.\n", + " \n", + " Returns NaN values.\n", + " \n", + " Parameters \n", + " ----------\n", + " alias: str\n", + " Custom name of the model. \n", + " \"\"\"\n", + " super().__init__(constant=np.nan, alias=alias)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "nanmodel = NaNModel()\n", + "nanmodel.forecast(ap, 12, level=[90, 80])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide\n", + "#test alias argument\n", + "test_eq(\n", + " repr(NaNModel()),\n", + " 'NaNModel'\n", + ")\n", + "test_eq(\n", + " repr(NaNModel(alias='NaN_custom')),\n", + " 'NaN_custom'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_doc(NaNModel, title_level=3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_doc(NaNModel.forecast, title_level=3, name='NaNModel.forecast')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_doc(NaNModel.fit, title_level=3, name='NaNModel.fit')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_doc(NaNModel.predict, title_level=3, name='NaNModel.predict')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "show_doc(NaNModel.predict_in_sample, title_level=3, name='NaNModel.predict_in_sample')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# NanModel's usage example\n", + "\n", + "from statsforecast.models import NaNModel\n", + "from statsforecast.utils import AirPassengers as ap\n", + "\n", + "\n", + "model = NaNModel()\n", + "model = model.fit(y=ap)\n", + "y_hat_dict = model.predict(h=4)\n", + "y_hat_dict" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/nbs/src/core/models_intro.qmd b/nbs/src/core/models_intro.qmd index 96d45fb22..b28601e01 100644 --- a/nbs/src/core/models_intro.qmd +++ b/nbs/src/core/models_intro.qmd @@ -8,10 +8,10 @@ Automatic forecasting tools search for the best parameters and select the best p |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| -|[`AutoARIMA`](../models.html#autoarima)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[`AutoETS`](../models.html#autoets)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[`AutoCES`](../models.html#autoces)|βœ…|βœ…|βœ…|βœ…|| -|[`AutoTheta`](../models.html#autotheta)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[`AutoARIMA`](./models.html#autoarima)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[`AutoETS`](./models.html#autoets)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[`AutoCES`](./models.html#autoces)|βœ…|βœ…|βœ…|βœ…|| +|[`AutoTheta`](./models.html#autotheta)|βœ…|βœ…|βœ…|βœ…|βœ…| : {tbl-colwidths="[75,25]"} ## ARIMA Family @@ -19,8 +19,8 @@ These models exploit the existing autocorrelations in the time series. |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| -|[`ARIMA`](../models.html#arima)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[`AutoRegressive`](../models.html#autoregressive)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[`ARIMA`](./models.html#arima)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[`AutoRegressive`](./models.html#autoregressive)|βœ…|βœ…|βœ…|βœ…|βœ…| : {tbl-colwidths="[75,25]"} ## Theta Family @@ -28,10 +28,10 @@ Fit two theta lines to a deseasonalized time series, using different techniques |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| -|[`Theta`](../models.html#theta)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[`OptimizedTheta`](../models.html#optimizedtheta)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[`DynamicTheta`](../models.html#dynamictheta)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[`DynamicOptimizedTheta`](../models.html#dynamicoptimizedtheta)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[`Theta`](./models.html#theta)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[`OptimizedTheta`](./models.html#optimizedtheta)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[`DynamicTheta`](./models.html#dynamictheta)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[`DynamicOptimizedTheta`](./models.html#dynamicoptimizedtheta)|βœ…|βœ…|βœ…|βœ…|βœ…| : {tbl-colwidths="[75,25]"} ## Multiple Seasonalities @@ -39,7 +39,7 @@ Suited for signals with more than one clear seasonality. Useful for low-frequenc |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| -|[`MSTL`](../models.html#mstl)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[`MSTL`](./models.html#mstl)|βœ…|βœ…|βœ…|βœ…|βœ…| : {tbl-colwidths="[75,25]"} ## GARCH and ARCH Models @@ -47,8 +47,8 @@ Suited for modeling time series that exhibit non-constant volatility over time. |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| -|[`GARCH`](../models.html#garch)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[`ARCH`](../models.html#arch)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[`GARCH`](./models.html#garch)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[`ARCH`](./models.html#arch)|βœ…|βœ…|βœ…|βœ…|βœ…| : {tbl-colwidths="[75,25]"} ## Baseline Models @@ -56,12 +56,12 @@ Classical models for establishing baseline. |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| -|[`HistoricAverage`](../models.html#historicaverage)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[`Naive`](../models.html#naive)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[`RandomWalkWithDrift`](../models.html#randomwalkwithdrift)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[`SeasonalNaive`](../models.html#seasonalnaive)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[`WindowAverage`](../models.html#windowaverage)|βœ…||||| -|[`SeasonalWindowAverage`](../models.html#seasonalwindowaverage)|βœ…||||| +|[`HistoricAverage`](./models.html#historicaverage)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[`Naive`](./models.html#naive)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[`RandomWalkWithDrift`](./models.html#randomwalkwithdrift)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[`SeasonalNaive`](./models.html#seasonalnaive)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[`WindowAverage`](./models.html#windowaverage)|βœ…||||| +|[`SeasonalWindowAverage`](./models.html#seasonalwindowaverage)|βœ…||||| : {tbl-colwidths="[75,25]"} ## Exponential Smoothing @@ -69,12 +69,12 @@ Uses a weighted average of all past observations where the weights decrease expo |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| -|[`SimpleExponentialSmoothing`](../models.html#simpleexponentialsmoothing)|βœ…||||| -|[`SimpleExponentialSmoothingOptimized`](../models.html#simpleexponentialsmoothingoptimized)|βœ…||||| -|[`SeasonalExponentialSmoothing`](../models.html#seasonalexponentialsmoothing)|βœ…||||| -|[`SeasonalExponentialSmoothingOptimized`](../models.html#seasonalexponentialsmoothingoptimized)|βœ…||||| -|[`Holt`](../models.html#holt)|βœ…|βœ…|βœ…|βœ…|βœ…| -|[`HoltWinters`](../models.html#holtwinters)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[`SimpleExponentialSmoothing`](./models.html#simpleexponentialsmoothing)|βœ…||||| +|[`SimpleExponentialSmoothingOptimized`](./models.html#simpleexponentialsmoothingoptimized)|βœ…||||| +|[`SeasonalExponentialSmoothing`](./models.html#seasonalexponentialsmoothing)|βœ…||||| +|[`SeasonalExponentialSmoothingOptimized`](./models.html#seasonalexponentialsmoothingoptimized)|βœ…||||| +|[`Holt`](./models.html#holt)|βœ…|βœ…|βœ…|βœ…|βœ…| +|[`HoltWinters`](./models.html#holtwinters)|βœ…|βœ…|βœ…|βœ…|βœ…| : {tbl-colwidths="[75,25]"} ## Sparse or Intermittent @@ -82,12 +82,12 @@ Suited for series with very few non-zero observations |Model | Point Forecast | Probabilistic Forecast | Insample fitted values | Probabilistic fitted values | |:------|:-------------:|:----------------------:|:---------------------:|:----------------------------:| -|[`ADIDA`](../models.html#adida)|βœ…||||| -|[`CrostonClassic`](../models.html#crostonclassic)|βœ…||||| -|[`CrostonOptimized`](../models.html#crostonoptimized)|βœ…||||| -|[`CrostonSBA`](../models.html#crostonsba)|βœ…||||| -|[`IMAPA`](../models.html#imapa)|βœ…||||| -|[`TSB`](../models.html#tsb)|βœ…||||| +|[`ADIDA`](./models.html#adida)|βœ…||||| +|[`CrostonClassic`](./models.html#crostonclassic)|βœ…||||| +|[`CrostonOptimized`](./models.html#crostonoptimized)|βœ…||||| +|[`CrostonSBA`](./models.html#crostonsba)|βœ…||||| +|[`IMAPA`](./models.html#imapa)|βœ…||||| +|[`TSB`](./models.html#tsb)|βœ…||||| : {tbl-colwidths="[75,25]"} diff --git a/statsforecast/_modidx.py b/statsforecast/_modidx.py index 3b8452a07..6331586c8 100644 --- a/statsforecast/_modidx.py +++ b/statsforecast/_modidx.py @@ -173,6 +173,8 @@ 'statsforecast/core.py'), 'statsforecast.core._StatsForecast._set_prediction_intervals': ( 'src/core/core.html#_statsforecast._set_prediction_intervals', 'statsforecast/core.py'), + 'statsforecast.core._StatsForecast._validate_model_names': ( 'src/core/core.html#_statsforecast._validate_model_names', + 'statsforecast/core.py'), 'statsforecast.core._StatsForecast.cross_validation': ( 'src/core/core.html#_statsforecast.cross_validation', 'statsforecast/core.py'), 'statsforecast.core._StatsForecast.cross_validation_fitted_values': ( 'src/core/core.html#_statsforecast.cross_validation_fitted_values', @@ -358,6 +360,20 @@ 'statsforecast/models.py'), 'statsforecast.models.AutoTheta.predict_in_sample': ( 'src/core/models.html#autotheta.predict_in_sample', 'statsforecast/models.py'), + 'statsforecast.models.ConstantModel': ( 'src/core/models.html#constantmodel', + 'statsforecast/models.py'), + 'statsforecast.models.ConstantModel.__init__': ( 'src/core/models.html#constantmodel.__init__', + 'statsforecast/models.py'), + 'statsforecast.models.ConstantModel.__repr__': ( 'src/core/models.html#constantmodel.__repr__', + 'statsforecast/models.py'), + 'statsforecast.models.ConstantModel.fit': ( 'src/core/models.html#constantmodel.fit', + 'statsforecast/models.py'), + 'statsforecast.models.ConstantModel.forecast': ( 'src/core/models.html#constantmodel.forecast', + 'statsforecast/models.py'), + 'statsforecast.models.ConstantModel.predict': ( 'src/core/models.html#constantmodel.predict', + 'statsforecast/models.py'), + 'statsforecast.models.ConstantModel.predict_in_sample': ( 'src/core/models.html#constantmodel.predict_in_sample', + 'statsforecast/models.py'), 'statsforecast.models.CrostonClassic': ( 'src/core/models.html#crostonclassic', 'statsforecast/models.py'), 'statsforecast.models.CrostonClassic.__init__': ( 'src/core/models.html#crostonclassic.__init__', @@ -470,6 +486,9 @@ 'statsforecast.models.MSTL.predict': ('src/core/models.html#mstl.predict', 'statsforecast/models.py'), 'statsforecast.models.MSTL.predict_in_sample': ( 'src/core/models.html#mstl.predict_in_sample', 'statsforecast/models.py'), + 'statsforecast.models.NaNModel': ('src/core/models.html#nanmodel', 'statsforecast/models.py'), + 'statsforecast.models.NaNModel.__init__': ( 'src/core/models.html#nanmodel.__init__', + 'statsforecast/models.py'), 'statsforecast.models.Naive': ('src/core/models.html#naive', 'statsforecast/models.py'), 'statsforecast.models.Naive.__init__': ( 'src/core/models.html#naive.__init__', 'statsforecast/models.py'), @@ -609,11 +628,20 @@ 'statsforecast/models.py'), 'statsforecast.models.WindowAverage.predict_in_sample': ( 'src/core/models.html#windowaverage.predict_in_sample', 'statsforecast/models.py'), + 'statsforecast.models.ZeroModel': ('src/core/models.html#zeromodel', 'statsforecast/models.py'), + 'statsforecast.models.ZeroModel.__init__': ( 'src/core/models.html#zeromodel.__init__', + 'statsforecast/models.py'), 'statsforecast.models._TS': ('src/core/models.html#_ts', 'statsforecast/models.py'), + 'statsforecast.models._TS._add_conformal_intervals': ( 'src/core/models.html#_ts._add_conformal_intervals', + 'statsforecast/models.py'), + 'statsforecast.models._TS._add_predict_conformal_intervals': ( 'src/core/models.html#_ts._add_predict_conformal_intervals', + 'statsforecast/models.py'), 'statsforecast.models._TS._conformal_method': ( 'src/core/models.html#_ts._conformal_method', 'statsforecast/models.py'), 'statsforecast.models._TS._conformity_scores': ( 'src/core/models.html#_ts._conformity_scores', 'statsforecast/models.py'), + 'statsforecast.models._TS._store_cs': ( 'src/core/models.html#_ts._store_cs', + 'statsforecast/models.py'), 'statsforecast.models._TS.new': ('src/core/models.html#_ts.new', 'statsforecast/models.py'), 'statsforecast.models._add_conformal_distribution_intervals': ( 'src/core/models.html#_add_conformal_distribution_intervals', 'statsforecast/models.py'), diff --git a/statsforecast/adapters/prophet.py b/statsforecast/adapters/prophet.py index 85204ef06..afda82587 100644 --- a/statsforecast/adapters/prophet.py +++ b/statsforecast/adapters/prophet.py @@ -120,8 +120,6 @@ def __init__( allowmean=False, blambda=None, biasadj=False, - parallel=False, - num_cores=2, period=1, ): Prophet.__init__( @@ -174,8 +172,6 @@ def __init__( allowmean=allowmean, blambda=blambda, biasadj=biasadj, - parallel=parallel, - num_cores=num_cores, period=period, ) diff --git a/statsforecast/arima.py b/statsforecast/arima.py index 3b6e7136a..ccb67f0f6 100644 --- a/statsforecast/arima.py +++ b/statsforecast/arima.py @@ -1306,8 +1306,6 @@ def search_arima( offset=None, allow_drift=True, allow_mean=True, - parallel=False, - num_cores=2, period=1, **kwargs ): @@ -1316,24 +1314,21 @@ def search_arima( allow_mean = allow_mean and (d + D) == 0 # max_K = allow_drift or allow_mean - if not parallel: - best_ic = np.inf - for i in range(max_p): - for j in range(max_q): - for I in range(max_P): - for J in range(max_Q): - if i + j + I + J > max_order: - continue - fit = myarima( - x, - order=(i, d, j), - seasonal={"order": (I, D, J), "period": m}, - ) - if fit["ic"] < best_ic: - best_ic = fit["ic"] - best_fit = fit - else: - raise NotImplementedError("parallel=True") + best_ic = np.inf + for i in range(max_p + 1): + for j in range(max_q + 1): + for I in range(max_P + 1): + for J in range(max_Q + 1): + if i + j + I + J > max_order: + continue + fit = myarima( + x, + order=(i, d, j), + seasonal={"order": (I, D, J), "period": m}, + ) + if fit["ic"] < best_ic: + best_ic = fit["ic"] + best_fit = fit return best_fit # %% ../nbs/src/arima.ipynb 54 @@ -1802,20 +1797,10 @@ def auto_arima_f( allowmean=True, blambda=None, biasadj=False, - parallel=False, - num_cores=2, period=1, ): if approximation is None: approximation = len(x) > 150 or period > 12 - if stepwise and parallel: - warnings.warn( - "Parallel computer is only implemented when stepwise=FALSE, the model will be fit in serial." - ) - parallel = False - if trace and parallel: - warnings.warn("Tracing model searching in parallel is not supported.") - trace = False if x.ndim > 1: raise ValueError("auto_arima can only handle univariate time series") if test_kwargs is None: @@ -2020,8 +2005,6 @@ def auto_arima_f( offset=offset, allowdrift=allowdrift, allowmean=allowmean, - parallel=parallel, - num_cores=num_cores, period=m, ) bestfit["lambda"] = blambda @@ -2468,15 +2451,6 @@ class AutoARIMA: a regular back transformation will result in median forecasts. If biasadj is True, an adjustment will be made to produce mean forecasts and fitted values. - parallel: bool (default False) - If True and stepwise = False, then the specification search - is done in parallel. - This can give a significant speedup on multicore machines. - num_cores: int (default 2) - Allows the user to specify the amount of parallel processes to be used - if parallel = True and stepwise = False. - If None, then the number of logical cores is - automatically detected and all available cores are used. period: int (default 1) Number of observations per unit of time. For example 24 for Hourly data. @@ -2522,8 +2496,6 @@ def __init__( allowmean: bool = True, blambda: Optional[float] = None, biasadj: bool = False, - parallel: bool = False, - num_cores: int = 2, period: int = 1, ): self.d = d @@ -2556,8 +2528,6 @@ def __init__( self.allowmean = allowmean self.blambda = blambda self.biasadj = biasadj - self.parallel = parallel - self.num_cores = num_cores self.period = period def fit(self, y: np.ndarray, X: Optional[np.ndarray] = None): @@ -2606,8 +2576,6 @@ def fit(self, y: np.ndarray, X: Optional[np.ndarray] = None): allowmean=self.allowmean, blambda=self.blambda, biasadj=self.biasadj, - parallel=self.parallel, - num_cores=self.num_cores, period=self.period, ) self.model_ = ARIMASummary(model_) diff --git a/statsforecast/core.py b/statsforecast/core.py index 8876c6dac..ddbaaf78a 100644 --- a/statsforecast/core.py +++ b/statsforecast/core.py @@ -13,7 +13,7 @@ from typing import Any, List, Optional, Union, Dict import pkg_resources -import fugue.api as fa +from fugue.execution.factory import make_execution_engine import matplotlib.pyplot as plt import matplotlib.colors as cm import numpy as np @@ -78,7 +78,7 @@ def fit(self, models): def _get_cols(self, models, attr, h, X, level=tuple()): n_models = len(models) - cuts = np.full(n_models + 1, fill_value=np.nan, dtype=np.int32) + cuts = np.full(n_models + 1, fill_value=0, dtype=np.int32) has_level_models = np.full(n_models, fill_value=False, dtype=bool) cuts[0] = 0 for i_model, model in enumerate(models): @@ -551,9 +551,10 @@ def _to_np_and_engine(self): # datetime check dt_arr = self.dataframe["ds"].to_numpy() processed_dt_arr = self._check_datetime(dt_arr) - self.dataframe = self.dataframe.with_columns( - pl.from_numpy(processed_dt_arr, schema=["ds"]) - ) + if type(dt_arr) != type(processed_dt_arr): + self.dataframe = self.dataframe.with_columns( + pl.from_numpy(processed_dt_arr.to_numpy(), schema=["ds"]) + ) sample_index_df = self.dataframe[self.non_value_columns] sorted_index_df = sample_index_df.sort(self.non_value_columns) @@ -792,7 +793,7 @@ def __init__( List of instantiated objects models.StatsForecast. freq : str Frequency of the data. - See [panda's available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases). + See [pandas' available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases). n_jobs : int (default=1) Number of jobs used in the parallel processing, use -1 for all cores. df : pandas.DataFrame or pl.DataFrame, optional (default=None) @@ -808,6 +809,7 @@ def __init__( # TODO @fede: needed for residuals, think about it later self.models = models + self._validate_model_names() self.freq = pd.tseries.frequencies.to_offset(freq) self.n_jobs = n_jobs self.fallback_model = fallback_model @@ -815,6 +817,15 @@ def __init__( self.n_jobs == 1 self._prepare_fit(df=df, sort_df=sort_df) + def _validate_model_names(self): + # Some test models don't have alias + names = [getattr(model, "alias", lambda: None) for model in self.models] + names = [x for x in names if x is not None] + if len(names) != len(set(names)): + raise ValueError( + "Model names must be unique. You can use `alias` to set a unique name for each model." + ) + def _prepare_fit(self, df, sort_df): if df is not None: df_process = DataFrameProcessing(df, sort_df) @@ -830,7 +841,8 @@ def _prepare_fit(self, df, sort_df): def _set_prediction_intervals(self, prediction_intervals): for model in self.models: - if hasattr(model, "prediction_intervals"): + interval = getattr(model, "prediction_intervals", None) + if interval is None: setattr(model, "prediction_intervals", prediction_intervals) def fit( @@ -898,6 +910,9 @@ def _make_future_df(self, h: int): def _parse_X_level(self, h, X, level): if X is not None: + if isinstance(X, pd.DataFrame): + if X.index.name != "unique_id": + X = X.set_index("unique_id") expected_shape_rows = h * len(self.ga) ga_shape = self.ga.data.shape[1] # Polars doesn't have index, hence, extra "column" @@ -1837,19 +1852,19 @@ def forecast( prediction_intervals=prediction_intervals, ) assert df is not None - with fa.engine_context(infer_by=[df]) as e: - backend = make_backend(e) - return backend.forecast( - df=df, - models=self.models, - freq=self.freq, - fallback_model=self.fallback_model, - h=h, - X_df=X_df, - level=level, - fitted=fitted, - prediction_intervals=prediction_intervals, - ) + engine = make_execution_engine(infer_by=[df]) + backend = make_backend(engine) + return backend.forecast( + df=df, + models=self.models, + freq=self.freq, + fallback_model=self.fallback_model, + h=h, + X_df=X_df, + level=level, + fitted=fitted, + prediction_intervals=prediction_intervals, + ) def cross_validation( self, @@ -1880,23 +1895,23 @@ def cross_validation( prediction_intervals=prediction_intervals, ) assert df is not None - with fa.engine_context(infer_by=[df]) as e: - backend = make_backend(e) - return backend.cross_validation( - df=df, - models=self.models, - freq=self.freq, - fallback_model=self.fallback_model, - h=h, - n_windows=n_windows, - step_size=step_size, - test_size=test_size, - input_size=input_size, - level=level, - refit=refit, - fitted=fitted, - prediction_intervals=prediction_intervals, - ) + engine = make_execution_engine(infer_by=[df]) + backend = make_backend(engine) + return backend.cross_validation( + df=df, + models=self.models, + freq=self.freq, + fallback_model=self.fallback_model, + h=h, + n_windows=n_windows, + step_size=step_size, + test_size=test_size, + input_size=input_size, + level=level, + refit=refit, + fitted=fitted, + prediction_intervals=prediction_intervals, + ) def _is_native(self, df) -> bool: engine = try_get_context_execution_engine() diff --git a/statsforecast/distributed/fugue.py b/statsforecast/distributed/fugue.py index e84745042..8d844fd24 100644 --- a/statsforecast/distributed/fugue.py +++ b/statsforecast/distributed/fugue.py @@ -4,6 +4,7 @@ __all__ = ['FugueBackend'] # %% ../../nbs/src/core/distributed.fugue.ipynb 4 +import inspect from typing import Any, Dict, List import numpy as np @@ -49,16 +50,16 @@ class FugueBackend(ParallelBackend): [Source code](https://github.com/Nixtla/statsforecast/blob/main/statsforecast/distributed/fugue.py). This class uses [Fugue](https://github.com/fugue-project/fugue) backend capable of distributing - computation on Spark and Dask without any rewrites. + computation on Spark, Dask and Ray without any rewrites. **Parameters:**
- `engine`: fugue.ExecutionEngine, a selection between spark and dask.
+ `engine`: fugue.ExecutionEngine, a selection between Spark, Dask, and Ray.
`conf`: fugue.Config, engine configuration.
`**transform_kwargs`: additional kwargs for Fugue's transform method.
**Notes:**
- A short introduction to Fugue, with examples on how to scale pandas code to scale pandas - based code to Spark or Dask is available [here](https://fugue-tutorials.readthedocs.io/tutorials/quick_look/ten_minutes.html). + A short introduction to Fugue, with examples on how to scale pandas code to Spark, Dask or Ray + is available [here](https://fugue-tutorials.readthedocs.io/tutorials/quick_look/ten_minutes.html). """ def __init__(self, engine: Any = None, conf: Any = None, **transform_kwargs: Any): @@ -85,7 +86,7 @@ def forecast( **Parameters:**
`df`: pandas.DataFrame, with columns [`unique_id`, `ds`, `y`] and exogenous.
- `freq`: str, frequency of the data, [panda's available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).
+ `freq`: str, frequency of the data, [pandas available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).
`models`: List[typing.Any], list of instantiated objects `StatsForecast.models`.
`fallback_model`: Any, Model to be used if a model fails.
`X_df`: pandas.DataFrame, with [unique_id, ds] columns and df’s future exogenous. @@ -97,13 +98,14 @@ def forecast( **References:**
For more information check the - [Fugue's transform](https://fugue-tutorials.readthedocs.io/tutorials/beginner/introduction.html#fugue-transform) + [Fugue's transform](https://fugue-tutorials.readthedocs.io/tutorials/beginner/transform.html) tutorial.
The [core.StatsForecast's forecast](https://nixtla.github.io/statsforecast/core.html#statsforecast.forecast) method documentation.
- Or the list of available [StatsForecast's models](https://nixtla.github.io/statsforecast/models.html). + Or the list of available [StatsForecast's models](https://nixtla.github.io/statsforecast/src/core/models.html). """ - schema = "*-y+" + str(self._get_output_schema(models)) + level = kwargs.get("level", []) + schema = "*-y+" + str(self._get_output_schema(models, level)) if X_df is None: return transform( df, @@ -121,7 +123,9 @@ def forecast( **self._transform_kwargs, ) else: - schema = "unique_id:str,ds:str," + str(self._get_output_schema(models)) + schema = "unique_id:str,ds:str," + str( + self._get_output_schema(models, level) + ) return _cotransform( df, X_df, @@ -173,7 +177,8 @@ def cross_validation( method documentation.
[Rob J. Hyndman and George Athanasopoulos (2018). "Forecasting principles and practice, Temporal Cross-Validation"](https://otexts.com/fpp3/tscv.html). """ - schema = "*-y+" + str(self._get_output_schema(models, mode="cv")) + level = kwargs.get("level", []) + schema = "*-y+" + str(self._get_output_schema(models, level, mode="cv")) return transform( df, self._cv, @@ -217,9 +222,21 @@ def _cv( ) return model.cross_validation(**kwargs).reset_index() - def _get_output_schema(self, models, mode="forecast") -> Schema: - cols: List[Any] - cols = [(repr(model), np.float32) for model in models] + def _get_output_schema(self, models, level=None, mode="forecast") -> Schema: + cols: List[Any] = [] + if level is None: + level = [] + for model in models: + has_levels = ( + "level" in inspect.signature(getattr(model, "forecast")).parameters + and len(level) > 0 + ) + cols.append((repr(model), np.float32)) + if has_levels: + cols.extend( + [(f"{repr(model)}-lo-{l}", np.float32) for l in reversed(level)] + ) + cols.extend([(f"{repr(model)}-hi-{l}", np.float32) for l in level]) if mode == "cv": cols = [("cutoff", "datetime"), ("y", np.float32)] + cols return Schema(cols) diff --git a/statsforecast/models.py b/statsforecast/models.py index 89450ea33..16bde47cb 100644 --- a/statsforecast/models.py +++ b/statsforecast/models.py @@ -6,7 +6,7 @@ 'SeasonalExponentialSmoothingOptimized', 'Holt', 'HoltWinters', 'HistoricAverage', 'Naive', 'RandomWalkWithDrift', 'SeasonalNaive', 'WindowAverage', 'SeasonalWindowAverage', 'ADIDA', 'CrostonClassic', 'CrostonOptimized', 'CrostonSBA', 'IMAPA', 'TSB', 'MSTL', 'Theta', 'OptimizedTheta', 'DynamicTheta', - 'DynamicOptimizedTheta', 'GARCH', 'ARCH'] + 'DynamicOptimizedTheta', 'GARCH', 'ARCH', 'ConstantModel', 'ZeroModel', 'NaNModel'] # %% ../nbs/src/core/models.ipynb 5 import warnings @@ -130,6 +130,20 @@ def _conformity_scores( def _conformal_method(self): return _get_conformal_method(self.prediction_intervals.method) + def _store_cs(self, y, X): + if self.prediction_intervals is not None: + self._cs = self._conformity_scores(y, X) + + def _add_conformal_intervals(self, fcst, y, X, level): + if self.prediction_intervals is not None and level is not None: + cs = self._conformity_scores(y, X) if y is not None else self._cs + res = self._conformal_method(fcst=fcst, cs=cs, level=level) + return res + return fcst + + def _add_predict_conformal_intervals(self, fcst, level): + return self._add_conformal_intervals(fcst=fcst, y=None, X=None, level=level) + # %% ../nbs/src/core/models.ipynb 16 class AutoARIMA(_TS): """AutoARIMA model. @@ -205,10 +219,6 @@ class AutoARIMA(_TS): Box-Cox transformation parameter. biasadj : bool Use adjusted back-transformed mean Box-Cox. - parallel : bool - If True and stepwise=False, then parallel search. - num_cores : int - Amount of parallel processes to be used if parallel=True. season_length : int Number of observations per unit of time. Ex: 24 Hourly data. alias : str @@ -251,8 +261,6 @@ def __init__( allowmean: bool = False, blambda: Optional[float] = None, biasadj: bool = False, - parallel: bool = False, - num_cores: int = 2, season_length: int = 1, alias: str = "AutoARIMA", prediction_intervals: Optional[ConformalIntervals] = None, @@ -287,8 +295,6 @@ def __init__( self.allowmean = allowmean self.blambda = blambda self.biasadj = biasadj - self.parallel = parallel - self.num_cores = num_cores self.season_length = season_length self.alias = alias self.prediction_intervals = prediction_intervals @@ -352,13 +358,10 @@ def fit( allowmean=self.allowmean, blambda=self.blambda, biasadj=self.biasadj, - parallel=self.parallel, - num_cores=self.num_cores, period=self.season_length, ) - if self.prediction_intervals is not None: - self._cs = self._conformity_scores(y=y, X=X) + self._store_cs(y=y, X=X) return self def predict( @@ -390,15 +393,16 @@ def predict( return res level = sorted(level) if self.prediction_intervals is not None: - res = self._conformal_method(fcst=res, cs=self._cs, level=level) - return res - return { - "mean": mean, - **{f"lo-{l}": fcst["lower"][f"{l}%"] for l in reversed(level)}, - **{f"hi-{l}": fcst["upper"][f"{l}%"] for l in level}, - } + res = self._add_predict_conformal_intervals(res, level) + else: + res = { + "mean": mean, + **{f"lo-{l}": fcst["lower"][f"{l}%"] for l in reversed(level)}, + **{f"hi-{l}": fcst["upper"][f"{l}%"] for l in level}, + } + return res - def predict_in_sample(self, level: Optional[Tuple[int]] = None): + def predict_in_sample(self, level: Optional[List[int]] = None): """Access fitted AutoArima insample predictions. Parameters @@ -487,8 +491,6 @@ def forecast( allowmean=self.allowmean, blambda=self.blambda, biasadj=self.biasadj, - parallel=self.parallel, - num_cores=self.num_cores, period=self.season_length, ) fcst = forecast_arima(mod, h, xreg=X_future, level=level) @@ -498,8 +500,7 @@ def forecast( if level is not None: level = sorted(level) if self.prediction_intervals is not None: - cs = self._conformity_scores(y=y, X=X) - res = self._conformal_method(fcst=res, cs=cs, level=level) + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) else: res = { **res, @@ -554,8 +555,7 @@ def forward( if level is not None: level = sorted(level) if self.prediction_intervals is not None: - cs = self._conformity_scores(y=y, X=X) - res = self._conformal_method(fcst=res, cs=cs, level=level) + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) else: res = { **res, @@ -602,6 +602,10 @@ class AutoETS(_TS): A parameter that 'dampens' the trend. alias : str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals], + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ def __init__( @@ -610,11 +614,13 @@ def __init__( model: str = "ZZZ", damped: Optional[bool] = None, alias: str = "AutoETS", + prediction_intervals: Optional[ConformalIntervals] = None, ): self.season_length = season_length self.model = model self.damped = damped self.alias = alias + self.prediction_intervals = prediction_intervals def __repr__(self): return self.alias @@ -634,7 +640,7 @@ def fit( y : numpy.array Clean time series of shape (t, ). X : array-like - Optional exogenpus of shape (t, n_x). + Optional exogenous of shape (t, n_x). Returns ------- @@ -645,6 +651,7 @@ def fit( y, m=self.season_length, model=self.model, damped=self.damped ) self.model_["actual_residuals"] = y - self.model_["fitted"] + self._store_cs(y=y, X=X) return self def predict( @@ -667,17 +674,21 @@ def predict( Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions. """ fcst = forecast_ets(self.model_, h=h, level=level) - mean = fcst["mean"] + res = {"mean": fcst["mean"]} if level is None: - return {"mean": mean} + return res level = sorted(level) - return { - "mean": mean, - **{f"lo-{l}": fcst[f"lo-{l}"] for l in reversed(level)}, - **{f"hi-{l}": fcst[f"hi-{l}"] for l in level}, - } + if self.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: + res = { + **res, + **{f"lo-{l}": fcst[f"lo-{l}"] for l in reversed(level)}, + **{f"hi-{l}": fcst[f"hi-{l}"] for l in level}, + } + return res - def predict_in_sample(self, level: Optional[Tuple[int]] = None): + def predict_in_sample(self, level: Optional[List[int]] = None): """Access fitted Exponential Smoothing insample predictions. Parameters @@ -740,11 +751,14 @@ def forecast( res = {key: fcst[key] for key in keys} if level is not None: level = sorted(level) - res = { - **res, - **{f"lo-{l}": fcst[f"lo-{l}"] for l in reversed(level)}, - **{f"hi-{l}": fcst[f"hi-{l}"] for l in level}, - } + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) + else: + res = { + **res, + **{f"lo-{l}": fcst[f"lo-{l}"] for l in reversed(level)}, + **{f"hi-{l}": fcst[f"hi-{l}"] for l in level}, + } if fitted: # add prediction intervals for fitted values se = _calculate_sigma(y - mod["fitted"], len(y) - mod["n_params"]) @@ -792,18 +806,21 @@ def forward( res = {key: fcst[key] for key in keys} if level is not None: level = sorted(level) - res = { - **res, - **{f"lo-{l}": fcst[f"lo-{l}"] for l in reversed(level)}, - **{f"hi-{l}": fcst[f"hi-{l}"] for l in level}, - } + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) + else: + res = { + **res, + **{f"lo-{l}": fcst[f"lo-{l}"] for l in reversed(level)}, + **{f"hi-{l}": fcst[f"hi-{l}"] for l in level}, + } if fitted: # add prediction intervals for fitted values se = _calculate_sigma(y - mod["fitted"], len(y) - mod["n_params"]) res = _add_fitted_pi(res=res, se=se, level=level) return res -# %% ../nbs/src/core/models.ipynb 44 +# %% ../nbs/src/core/models.ipynb 45 class ETS(AutoETS): @classmethod def _warn(cls): @@ -819,17 +836,19 @@ def __init__( model: str = "ZZZ", damped: Optional[bool] = None, alias: str = "ETS", + prediction_intervals: Optional[ConformalIntervals] = None, ): ETS._warn() self.season_length = season_length self.model = model self.damped = damped self.alias = alias + self.prediction_intervals = prediction_intervals def __repr__(self): return self.alias -# %% ../nbs/src/core/models.ipynb 49 +# %% ../nbs/src/core/models.ipynb 50 class AutoCES(_TS): """Complex Exponential Smoothing model. @@ -901,8 +920,7 @@ def fit( """ self.model_ = auto_ces(y, m=self.season_length, model=self.model) self.model_["actual_residuals"] = y - self.model_["fitted"] - if self.prediction_intervals is not None: - self._cs = self._conformity_scores(y=y, X=X) + self._store_cs(y=y, X=X) return self def predict( @@ -925,21 +943,21 @@ def predict( Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions. """ fcst = forecast_ces(self.model_, h=h, level=level) - mean = fcst["mean"] - res = {"mean": mean} + res = {"mean": fcst["mean"]} if level is None: return res level = sorted(level) if self.prediction_intervals is not None: - res = self._conformal_method(fcst=res, cs=self._cs, level=level) - return res - return { - "mean": mean, - **{f"lo-{l}": fcst[f"lo-{l}"] for l in reversed(level)}, - **{f"hi-{l}": fcst[f"hi-{l}"] for l in level}, - } + res = self._add_predict_conformal_intervals(res, level) + else: + res = { + **res, + **{f"lo-{l}": fcst[f"lo-{l}"] for l in reversed(level)}, + **{f"hi-{l}": fcst[f"hi-{l}"] for l in level}, + } + return res - def predict_in_sample(self, level: Optional[Tuple[int]] = None): + def predict_in_sample(self, level: Optional[List[int]] = None): """Access fitted Exponential Smoothing insample predictions. Parameters @@ -1003,8 +1021,7 @@ def forecast( if level is not None: level = sorted(level) if self.prediction_intervals is not None: - cs = self._conformity_scores(y=y, X=X) - res = self._conformal_method(fcst=res, cs=cs, level=level) + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) else: res = { **res, @@ -1059,8 +1076,7 @@ def forward( if level is not None: level = sorted(level) if self.prediction_intervals is not None: - cs = self._conformity_scores(y=y, X=X) - res = self._conformal_method(fcst=res, cs=cs, level=level) + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) else: res = { **res, @@ -1073,7 +1089,7 @@ def forward( res = _add_fitted_pi(res=res, se=se, level=level) return res -# %% ../nbs/src/core/models.ipynb 66 +# %% ../nbs/src/core/models.ipynb 67 class AutoTheta(_TS): """AutoTheta model. @@ -1146,15 +1162,14 @@ def fit( decomposition_type=self.decomposition_type, ) self.model_["fitted"] = y - self.model_["residuals"] - if self.prediction_intervals is not None: - self._cs = self._conformity_scores(y=y, X=X) + self._store_cs(y, X) return self def predict( self, h: int, X: Optional[np.ndarray] = None, - level: Optional[Tuple[int]] = None, + level: Optional[List[int]] = None, ): """Predict with fitted AutoTheta. @@ -1174,10 +1189,10 @@ def predict( """ fcst = forecast_theta(self.model_, h=h, level=level) if self.prediction_intervals is not None and level is not None: - fcst = self._conformal_method(fcst=fcst, cs=self._cs, level=level) + fcst = self._add_predict_conformal_intervals(fcst, level) return fcst - def predict_in_sample(self, level: Optional[Tuple[int]] = None): + def predict_in_sample(self, level: Optional[List[int]] = None): """Access fitted AutoTheta insample predictions. Parameters @@ -1238,8 +1253,8 @@ def forecast( decomposition_type=self.decomposition_type, ) res = forecast_theta(mod, h, level=level) - if self.prediction_intervals is not None and level is not None: - res = self._conformal_method(fcst=res, cs=self._cs, level=level) + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) if fitted: res["fitted"] = y - mod["residuals"] if level is not None and fitted: @@ -1283,8 +1298,8 @@ def forward( raise Exception("You have to use the `fit` method first") mod = forward_theta(self.model_, y=y) res = forecast_theta(mod, h, level=level) - if self.prediction_intervals is not None and level is not None: - res = self._conformal_method(fcst=res, cs=self._cs, level=level) + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) if fitted: res["fitted"] = y - mod["residuals"] if level is not None and fitted: @@ -1408,8 +1423,7 @@ def fit( method=self.method, fixed=self.fixed, ) - if self.prediction_intervals is not None: - self._cs = self._conformity_scores(y=y, X=X) + self._store_cs(y=y, X=X) return self def predict( @@ -1441,15 +1455,16 @@ def predict( return res level = sorted(level) if self.prediction_intervals is not None: - res = self._conformal_method(fcst=res, cs=self._cs, level=level) - return res - return { - "mean": mean, - **{f"lo-{l}": fcst["lower"][f"{l}%"] for l in reversed(level)}, - **{f"hi-{l}": fcst["upper"][f"{l}%"] for l in level}, - } + res = self._add_predict_conformal_intervals(res, level) + else: + res = { + "mean": mean, + **{f"lo-{l}": fcst["lower"][f"{l}%"] for l in reversed(level)}, + **{f"hi-{l}": fcst["upper"][f"{l}%"] for l in level}, + } + return res - def predict_in_sample(self, level: Optional[Tuple[int]] = None): + def predict_in_sample(self, level: Optional[List[int]] = None): """Access fitted insample predictions. Parameters @@ -1525,8 +1540,7 @@ def forecast( if level is not None: level = sorted(level) if self.prediction_intervals is not None: - cs = self._conformity_scores(y=y, X=X) - res = self._conformal_method(fcst=res, cs=cs, level=level) + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) else: res = { **res, @@ -1581,8 +1595,7 @@ def forward( if level is not None: level = sorted(level) if self.prediction_intervals is not None: - cs = self._conformity_scores(y=y, X=X) - res = self._conformal_method(fcst=res, cs=cs, level=level) + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) else: res = { **res, @@ -1595,7 +1608,7 @@ def forward( res = _add_fitted_pi(res=res, se=se, level=level) return res -# %% ../nbs/src/core/models.ipynb 97 +# %% ../nbs/src/core/models.ipynb 96 class AutoRegressive(ARIMA): """Simple Autoregressive model. @@ -1670,7 +1683,7 @@ def __init__( def __repr__(self): return self.alias -# %% ../nbs/src/core/models.ipynb 112 +# %% ../nbs/src/core/models.ipynb 110 @njit def _ses_fcst_mse(x: np.ndarray, alpha: float) -> Tuple[float, float, np.ndarray]: """Perform simple exponential smoothing on a series. @@ -1756,7 +1769,7 @@ def _chunk_sums(array: np.ndarray, chunk_size: int) -> np.ndarray: sums[i] = array[start : start + chunk_size].sum() return sums -# %% ../nbs/src/core/models.ipynb 113 +# %% ../nbs/src/core/models.ipynb 111 @njit def _ses( y: np.ndarray, # time series @@ -1771,7 +1784,7 @@ def _ses( fcst["fitted"] = fitted_vals return fcst -# %% ../nbs/src/core/models.ipynb 114 +# %% ../nbs/src/core/models.ipynb 112 class SimpleExponentialSmoothing(_TS): """SimpleExponentialSmoothing model. @@ -1790,11 +1803,22 @@ class SimpleExponentialSmoothing(_TS): Smoothing parameter. alias : str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ - def __init__(self, alpha: float, alias: str = "SES"): + def __init__( + self, + alpha: float, + alias: str = "SES", + prediction_intervals: Optional[ConformalIntervals] = None, + ): self.alpha = alpha self.alias = alias + self.prediction_intervals = prediction_intervals + self.only_conformal_intervals = True def __repr__(self): return self.alias @@ -1823,12 +1847,14 @@ def fit( """ mod = _ses(y=y, alpha=self.alpha, h=1, fitted=True) self.model_ = dict(mod) + self._store_cs(y=y, X=X) return self def predict( self, h: int, X: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, ): """Predict with fitted SimpleExponentialSmoothing. @@ -1838,6 +1864,8 @@ def predict( Forecast horizon. X : array-like Optional insample exogenous of shape (t, n_x). + level : List[float] + Confidence levels (0-100) for prediction intervals. Returns ------- @@ -1846,6 +1874,13 @@ def predict( """ mean = _repeat_val(val=self.model_["mean"][0], h=h) res = {"mean": mean} + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: + raise Exception("You must pass `prediction_intervals` to " "compute them.") return res def predict_in_sample(self): @@ -1871,6 +1906,7 @@ def forecast( h: int, X: Optional[np.ndarray] = None, X_future: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, fitted: bool = False, ): """Memory Efficient SimpleExponentialSmoothing predictions. @@ -1899,10 +1935,18 @@ def forecast( forecasts : dict Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions. """ - out = _ses(y=y, h=h, fitted=fitted, alpha=self.alpha) - return out + res = _ses(y=y, h=h, fitted=fitted, alpha=self.alpha) + res = dict(res) + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) + else: + raise Exception("You must pass `prediction_intervals` to " "compute them.") + return res -# %% ../nbs/src/core/models.ipynb 124 +# %% ../nbs/src/core/models.ipynb 123 def _ses_optimized( y: np.ndarray, # time series h: int, # forecasting horizon @@ -1915,7 +1959,7 @@ def _ses_optimized( fcst["fitted"] = fitted_vals return fcst -# %% ../nbs/src/core/models.ipynb 125 +# %% ../nbs/src/core/models.ipynb 124 class SimpleExponentialSmoothingOptimized(_TS): """SimpleExponentialSmoothing model. @@ -1932,10 +1976,20 @@ class SimpleExponentialSmoothingOptimized(_TS): ---------- alias: str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ - def __init__(self, alias: str = "SESOpt"): + def __init__( + self, + alias: str = "SESOpt", + prediction_intervals: Optional[ConformalIntervals] = None, + ): self.alias = alias + self.prediction_intervals = prediction_intervals + self.only_conformal_intervals = True def __repr__(self): return self.alias @@ -1964,12 +2018,14 @@ def fit( """ mod = _ses_optimized(y=y, h=1, fitted=True) self.model_ = dict(mod) + self._store_cs(y=y, X=X) return self def predict( self, h: int, X: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, ): """Predict with fitted SimpleExponentialSmoothingOptimized. @@ -1979,6 +2035,8 @@ def predict( Forecast horizon. X : array-like Optional insample exogenous of shape (t, n_x). + level : List[float] + Confidence levels (0-100) for prediction intervals. Returns ------- @@ -1987,6 +2045,13 @@ def predict( """ mean = _repeat_val(val=self.model_["mean"][0], h=h) res = {"mean": mean} + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: + raise Exception("You must pass `prediction_intervals` to " "compute them.") return res def predict_in_sample(self): @@ -2011,6 +2076,7 @@ def forecast( h: int, X: Optional[np.ndarray] = None, X_future: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, fitted: bool = False, ): """Memory Efficient SimpleExponentialSmoothingOptimized predictions. @@ -2039,8 +2105,16 @@ def forecast( forecasts : dict Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions. """ - out = _ses_optimized(y=y, h=h, fitted=fitted) - return out + res = _ses_optimized(y=y, h=h, fitted=fitted) + res = dict(res) + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) + else: + raise Exception("You must pass `prediction_intervals` to compute them.") + return res # %% ../nbs/src/core/models.ipynb 135 @njit @@ -2082,7 +2156,6 @@ class SeasonalExponentialSmoothing(_TS): **References:**
[Charles. C. Holt (1957). "Forecasting seasonals and trends by exponentially weighted moving averages", ONR Research Memorandum, Carnegie Institute of Technology 52.](https://www.sciencedirect.com/science/article/abs/pii/S0169207003001134). - [Peter R. Winters (1960). "Forecasting sales by exponentially weighted moving averages". Management Science](https://pubsonline.informs.org/doi/abs/10.1287/mnsc.6.3.324). Parameters @@ -2093,12 +2166,24 @@ class SeasonalExponentialSmoothing(_TS): Number of observations per unit of time. Ex: 24 Hourly data. alias : str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ - def __init__(self, season_length: int, alpha: float, alias: str = "SeasonalES"): + def __init__( + self, + season_length: int, + alpha: float, + alias: str = "SeasonalES", + prediction_intervals: Optional[ConformalIntervals] = None, + ): self.season_length = season_length self.alpha = alpha self.alias = alias + self.prediction_intervals = prediction_intervals + self.only_conformal_intervals = True def __repr__(self): return self.alias @@ -2133,12 +2218,14 @@ def fit( h=self.season_length, ) self.model_ = dict(mod) + self._store_cs(y=y, X=X) return self def predict( self, h: int, X: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, ): """Predict with fitted SeasonalExponentialSmoothing. @@ -2148,6 +2235,8 @@ def predict( Forecast horizon. X : array-like Optional insample exogenous of shape (t, n_x). + level : List[float] + Confidence levels (0-100) for prediction intervals. Returns ------- @@ -2158,6 +2247,13 @@ def predict( self.model_["mean"], season_length=self.season_length, h=h ) res = {"mean": mean} + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: + raise Exception("You must pass `prediction_intervals` to compute them.") return res def predict_in_sample(self): @@ -2182,6 +2278,7 @@ def forecast( h: int, X: Optional[np.ndarray] = None, X_future: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, fitted: bool = False, ): """Memory Efficient SeasonalExponentialSmoothing predictions. @@ -2210,12 +2307,20 @@ def forecast( forecasts : dict Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions. """ - out = _seasonal_exponential_smoothing( + res = _seasonal_exponential_smoothing( y=y, h=h, fitted=fitted, alpha=self.alpha, season_length=self.season_length ) - return out + res = dict(res) + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) + else: + raise Exception("You must pass `prediction_intervals` to compute them.") + return res -# %% ../nbs/src/core/models.ipynb 149 +# %% ../nbs/src/core/models.ipynb 150 def _seasonal_ses_optimized( y: np.ndarray, # time series h: int, # forecasting horizon @@ -2238,9 +2343,14 @@ def _seasonal_ses_optimized( fcst["fitted"] = fitted_vals return fcst -# %% ../nbs/src/core/models.ipynb 150 +# %% ../nbs/src/core/models.ipynb 151 class SeasonalExponentialSmoothingOptimized(_TS): - def __init__(self, season_length: int, alias: str = "SeasESOpt"): + def __init__( + self, + season_length: int, + alias: str = "SeasESOpt", + prediction_intervals: Optional[ConformalIntervals] = None, + ): """SeasonalExponentialSmoothingOptimized model. Uses a weighted average of all past observations where the weights decrease exponentially into the past. @@ -2256,17 +2366,23 @@ def __init__(self, season_length: int, alias: str = "SeasESOpt"): **References:**
[Charles. C. Holt (1957). "Forecasting seasonals and trends by exponentially weighted moving averages", ONR Research Memorandum, Carnegie Institute of Technology 52.](https://www.sciencedirect.com/science/article/abs/pii/S0169207003001134). - [Peter R. Winters (1960). "Forecasting sales by exponentially weighted moving averages". Management Science](https://pubsonline.informs.org/doi/abs/10.1287/mnsc.6.3.324). Parameters + ---------- season_length : int Number of observations per unit of time. Ex: 24 Hourly data. alias : str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ self.season_length = season_length self.alias = alias + self.prediction_intervals = prediction_intervals + self.only_conformal_intervals = True def __repr__(self): return self.alias @@ -2300,12 +2416,14 @@ def fit( h=self.season_length, ) self.model_ = dict(mod) + self._store_cs(y=y, X=X) return self def predict( self, h: int, X: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, ): """Predict with fitted SeasonalExponentialSmoothingOptimized. @@ -2315,6 +2433,8 @@ def predict( Forecast horizon. X : array-like Optional insample exogenous of shape (t, n_x). + level : List[float] + Confidence levels (0-100) for prediction intervals. Returns ------- @@ -2325,6 +2445,13 @@ def predict( self.model_["mean"], season_length=self.season_length, h=h ) res = {"mean": mean} + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: + raise Exception("You must pass `prediction_intervals` to compute them.") return res def predict_in_sample(self): @@ -2349,6 +2476,7 @@ def forecast( h: int, X: Optional[np.ndarray] = None, X_future: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, fitted: bool = False, ): """Memory Efficient SeasonalExponentialSmoothingOptimized predictions. @@ -2377,12 +2505,20 @@ def forecast( forecasts : dict Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions. """ - out = _seasonal_ses_optimized( + res = _seasonal_ses_optimized( y=y, h=h, fitted=fitted, season_length=self.season_length ) - return out + res = dict(res) + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) + else: + raise Exception("You must pass `prediction_intervals` to compute them.") + return res -# %% ../nbs/src/core/models.ipynb 161 +# %% ../nbs/src/core/models.ipynb 163 class Holt(AutoETS): """Holt's method. @@ -2400,21 +2536,32 @@ class Holt(AutoETS): The type of error of the ETS model. Can be additive (A) or multiplicative (M). alias : str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ def __init__( - self, season_length: int = 1, error_type: str = "A", alias: str = "Holt" + self, + season_length: int = 1, + error_type: str = "A", + alias: str = "Holt", + prediction_intervals: Optional[ConformalIntervals] = None, ): self.season_length = season_length self.error_type = error_type self.alias = alias + self.prediction_intervals = prediction_intervals model = error_type + "AN" - super().__init__(season_length, model, alias=alias) + super().__init__( + season_length, model, alias=alias, prediction_intervals=prediction_intervals + ) def __repr__(self): return self.alias -# %% ../nbs/src/core/models.ipynb 173 +# %% ../nbs/src/core/models.ipynb 176 class HoltWinters(AutoETS): """Holt-Winters' method. @@ -2432,6 +2579,10 @@ class HoltWinters(AutoETS): The type of error of the ETS model. Can be additive (A) or multiplicative (M). alias : str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ def __init__( @@ -2439,17 +2590,20 @@ def __init__( season_length: int = 1, # season length error_type: str = "A", # error type alias: str = "HoltWinters", + prediction_intervals: Optional[ConformalIntervals] = None, ): self.season_length = season_length self.error_type = error_type self.alias = alias model = error_type + "A" + error_type - super().__init__(season_length, model, alias=alias) + super().__init__( + season_length, model, alias=alias, prediction_intervals=prediction_intervals + ) def __repr__(self): return self.alias -# %% ../nbs/src/core/models.ipynb 186 +# %% ../nbs/src/core/models.ipynb 190 @njit def _historic_average( y: np.ndarray, # time series @@ -2465,9 +2619,13 @@ def _historic_average( fcst["fitted"] = fitted_vals return fcst -# %% ../nbs/src/core/models.ipynb 187 +# %% ../nbs/src/core/models.ipynb 191 class HistoricAverage(_TS): - def __init__(self, alias: str = "HistoricAverage"): + def __init__( + self, + alias: str = "HistoricAverage", + prediction_intervals: Optional[ConformalIntervals] = None, + ): """HistoricAverage model. Also known as mean method. Uses a simple average of all past observations. @@ -2481,8 +2639,13 @@ def __init__(self, alias: str = "HistoricAverage"): ---------- alias: str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ self.alias = alias + self.prediction_intervals = prediction_intervals def __repr__(self): return self.alias @@ -2514,13 +2677,14 @@ def fit( mod["sigma"] = _calculate_sigma(residuals, len(residuals) - 1) mod["n"] = len(y) self.model_ = mod + self._store_cs(y=y, X=X) return self def predict( self, h: int, X: Optional[np.ndarray] = None, - level: Optional[Tuple[int]] = None, + level: Optional[List[int]] = None, ): """Predict with fitted HistoricAverage. @@ -2533,7 +2697,6 @@ def predict( level : List[float] Confidence levels (0-100) for prediction intervals. - Returns ------- forecasts : dict @@ -2542,7 +2705,12 @@ def predict( mean = _repeat_val(val=self.model_["mean"][0], h=h) res = {"mean": mean} - if level is not None: + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: sigma = self.model_["sigma"] sigmah = sigma * np.sqrt(1 + (1 / self.model_["n"])) pred_int = _calculate_intervals(res, level, h, sigmah) @@ -2550,7 +2718,7 @@ def predict( return res - def predict_in_sample(self, level: Optional[Tuple[int]] = None): + def predict_in_sample(self, level: Optional[List[int]] = None): """Access fitted HistoricAverage insample predictions. Parameters @@ -2576,7 +2744,7 @@ def forecast( h: int, X: Optional[np.ndarray] = None, X_future: Optional[np.ndarray] = None, - level: Optional[Tuple[int]] = None, + level: Optional[List[int]] = None, fitted: bool = False, ): """Memory Efficient HistoricAverage predictions. @@ -2610,24 +2778,35 @@ def forecast( if fitted: res["fitted"] = out["fitted"] - if level is not None: - residuals = y - out["fitted"] - sigma = _calculate_sigma(residuals, len(residuals) - 1) - sigmah = sigma * np.sqrt(1 + (1 / len(y))) - pred_int = _calculate_intervals(out, level, h, sigmah) - res = {**res, **pred_int} + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) + else: + residuals = y - out["fitted"] + sigma = _calculate_sigma(residuals, len(residuals) - 1) + sigmah = sigma * np.sqrt(1 + (1 / len(y))) + pred_int = _calculate_intervals(out, level, h, sigmah) + res = {**res, **pred_int} if fitted: + residuals = y - out["fitted"] + sigma = _calculate_sigma(residuals, len(residuals) - 1) + sigmah = sigma * np.sqrt(1 + (1 / len(y))) res = _add_fitted_pi(res=res, se=sigmah, level=level) return res -# %% ../nbs/src/core/models.ipynb 198 +# %% ../nbs/src/core/models.ipynb 203 class Naive(_TS): - def __init__(self, alias: str = "Naive"): + def __init__( + self, + alias: str = "Naive", + prediction_intervals: Optional[ConformalIntervals] = None, + ): """Naive model. - A random walk model, defined as $\hat{y}_{t+1} = y_t$ for all $t$ + All forecasts have the value of the last observation: + $\hat{y}_{t+1} = y_t$ for all $t$ **References:**
[Rob J. Hyndman and George Athanasopoulos (2018). "forecasting principles and practice, Simple Methods"](https://otexts.com/fpp3/simple-methods.html). @@ -2636,8 +2815,13 @@ def __init__(self, alias: str = "Naive"): ---------- alias: str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ self.alias = alias + self.prediction_intervals = prediction_intervals def __repr__(self): return self.alias @@ -2669,13 +2853,14 @@ def fit( sigma = _calculate_sigma(residuals, len(residuals) - 1) mod["sigma"] = sigma self.model_ = mod + self._store_cs(y=y, X=X) return self def predict( self, h: int, # forecasting horizon X: Optional[np.ndarray] = None, # exogenous regressors - level: Optional[Tuple[int]] = None, # confidence level + level: Optional[List[int]] = None, # confidence level ): """Predict with fitted Naive. @@ -2696,16 +2881,20 @@ def predict( mean = _repeat_val(self.model_["mean"][0], h=h) res = {"mean": mean} - if level is not None: + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: steps = np.arange(1, h + 1) sigma = self.model_["sigma"] sigmah = sigma * np.sqrt(steps) pred_int = _calculate_intervals(res, level, h, sigmah) res = {**res, **pred_int} - return res - def predict_in_sample(self, level: Optional[Tuple[int]] = None): + def predict_in_sample(self, level: Optional[List[int]] = None): """Access fitted Naive insample predictions. Parameters @@ -2729,7 +2918,7 @@ def forecast( h: int, X: Optional[np.ndarray] = None, X_future: Optional[np.ndarray] = None, - level: Optional[Tuple[int]] = None, + level: Optional[List[int]] = None, fitted: bool = False, ): """Memory Efficient Naive predictions. @@ -2760,23 +2949,26 @@ def forecast( """ out = _naive(y=y, h=h, fitted=fitted or (level is not None)) res = {"mean": out["mean"]} - if fitted: res["fitted"] = out["fitted"] - if level is not None: - steps = np.arange(1, h + 1) - residuals = y - out["fitted"] - sigma = _calculate_sigma(residuals, len(residuals) - 1) - sigmah = sigma * np.sqrt(steps) - pred_int = _calculate_intervals(out, level, h, sigmah) - res = {**res, **pred_int} + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) + else: + steps = np.arange(1, h + 1) + residuals = y - out["fitted"] + sigma = _calculate_sigma(residuals, len(residuals) - 1) + sigmah = sigma * np.sqrt(steps) + pred_int = _calculate_intervals(out, level, h, sigmah) + res = {**res, **pred_int} if fitted: + residuals = y - out["fitted"] + sigma = _calculate_sigma(residuals, len(residuals) - 1) res = _add_fitted_pi(res=res, se=sigma, level=level) - return res -# %% ../nbs/src/core/models.ipynb 211 +# %% ../nbs/src/core/models.ipynb 217 @njit def _random_walk_with_drift( y: np.ndarray, # time series @@ -2796,9 +2988,13 @@ def _random_walk_with_drift( fcst["fitted"] = fitted_vals return fcst -# %% ../nbs/src/core/models.ipynb 212 +# %% ../nbs/src/core/models.ipynb 218 class RandomWalkWithDrift(_TS): - def __init__(self, alias: str = "RWD"): + def __init__( + self, + alias: str = "RWD", + prediction_intervals: Optional[ConformalIntervals] = None, + ): """RandomWalkWithDrift model. A variation of the naive method allows the forecasts to change over time. @@ -2816,8 +3012,13 @@ def __init__(self, alias: str = "RWD"): ---------- alias : str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ self.alias = alias + self.prediction_intervals = prediction_intervals def __repr__(self): return self.alias @@ -2848,10 +3049,11 @@ def fit( mod["sigma"] = sigma mod["n"] = len(y) self.model_ = mod + self._store_cs(y=y, X=X) return self def predict( - self, h: int, X: Optional[np.ndarray] = None, level: Optional[Tuple[int]] = None + self, h: int, X: Optional[np.ndarray] = None, level: Optional[List[int]] = None ): """Predict with fitted RandomWalkWithDrift. @@ -2873,16 +3075,20 @@ def predict( mean = self.model_["slope"] * (1 + hrange) + self.model_["last_y"] res = {"mean": mean} - if level is not None: + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: steps = np.arange(1, h + 1) sigma = self.model_["sigma"] sigmah = sigma * np.sqrt(steps * (1 + steps / (self.model_["n"] - 1))) pred_int = _calculate_intervals(res, level, h, sigmah) res = {**res, **pred_int} - return res - def predict_in_sample(self, level: Optional[Tuple[int]] = None): + def predict_in_sample(self, level: Optional[List[int]] = None): """Access fitted RandomWalkWithDrift insample predictions. Parameters @@ -2906,7 +3112,7 @@ def forecast( h: int, X: Optional[np.ndarray] = None, X_future: Optional[np.ndarray] = None, - level: Optional[Tuple[int]] = None, + level: Optional[List[int]] = None, fitted: bool = False, ): """Memory Efficient RandomWalkWithDrift predictions. @@ -2942,36 +3148,52 @@ def forecast( res["fitted"] = out["fitted"] if level is not None: - steps = np.arange(1, h + 1) - residuals = y - out["fitted"] - sigma = _calculate_sigma(residuals, len(residuals) - 1) - sigmah = sigma * np.sqrt(steps * (1 + steps / (len(y) - 1))) - pred_int = _calculate_intervals(out, level, h, sigmah) - res = {**res, **pred_int} + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) + else: + steps = np.arange(1, h + 1) + residuals = y - out["fitted"] + sigma = _calculate_sigma(residuals, len(residuals) - 1) + sigmah = sigma * np.sqrt(steps * (1 + steps / (len(y) - 1))) + pred_int = _calculate_intervals(out, level, h, sigmah) + res = {**res, **pred_int} if fitted: + residuals = y - out["fitted"] + sigma = _calculate_sigma(residuals, len(residuals) - 1) res = _add_fitted_pi(res=res, se=sigma, level=level) return res -# %% ../nbs/src/core/models.ipynb 225 +# %% ../nbs/src/core/models.ipynb 232 class SeasonalNaive(_TS): - def __init__(self, season_length: int, alias: str = "SeasonalNaive"): - self.season_length = season_length - self.alias = alias + def __init__( + self, + season_length: int, + alias: str = "SeasonalNaive", + prediction_intervals: Optional[ConformalIntervals] = None, + ): """Seasonal naive model. - A method similar to the naive, but uses the last known observation of the same period (e.g. the same month of the previous year) in order to capture seasonal variations. + A method similar to the naive, but uses the last known observation of the same period (e.g. the same month of the previous year) in order to capture seasonal variations. **References:**
- [Rob J. Hyndman and George Athanasopoulos (2018). "forecasting principles and practice, Simple Methods"](https://otexts.com/fpp3/simple-methods.html). + [Rob J. Hyndman and George Athanasopoulos (2018). "forecasting principles and practice, Simple Methods"](https://otexts.com/fpp3/simple-methods.html#seasonal-na%C3%AFve-method). Parameters ---------- - season_length : int + season_length : int Number of observations per unit of time. Ex: 24 Hourly data. - alias : str + alias : str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ + self.season_length = season_length + self.alias = alias + self.prediction_intervals = prediction_intervals def __repr__(self): return self.alias @@ -3007,13 +3229,14 @@ def fit( residuals = y - mod["fitted"] mod["sigma"] = _calculate_sigma(residuals, len(y) - self.season_length) self.model_ = mod + self._store_cs(y=y, X=X) return self def predict( self, h: int, X: Optional[np.ndarray] = None, - level: Optional[Tuple[int]] = None, + level: Optional[List[int]] = None, ): """Predict with fitted Naive. @@ -3036,16 +3259,19 @@ def predict( ) res = {"mean": mean} - if level is not None: + if level is None: + return res + if self.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: k = np.floor((h - 1) / self.season_length) sigma = self.model_["sigma"] sigmah = sigma * np.sqrt(k + 1) pred_int = _calculate_intervals(res, level, h, sigmah) res = {**res, **pred_int} - return res - def predict_in_sample(self, level: Optional[Tuple[int]] = None): + def predict_in_sample(self, level: Optional[List[int]] = None): """Access fitted SeasonalNaive insample predictions. Parameters @@ -3069,7 +3295,7 @@ def forecast( h: int, X: Optional[np.ndarray] = None, X_future: Optional[np.ndarray] = None, - level: Optional[Tuple[int]] = None, + level: Optional[List[int]] = None, fitted: bool = False, ): """Memory Efficient SeasonalNaive predictions. @@ -3105,23 +3331,28 @@ def forecast( season_length=self.season_length, ) res = {"mean": out["mean"]} - if fitted: res["fitted"] = out["fitted"] - if level is not None: - k = np.floor((h - 1) / self.season_length) - residuals = y - out["fitted"] - sigma = _calculate_sigma(residuals, len(y) - self.season_length) - sigmah = sigma * np.sqrt(k + 1) - pred_int = _calculate_intervals(out, level, h, sigmah) - res = {**res, **pred_int} + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) + else: + k = np.floor((h - 1) / self.season_length) + residuals = y - out["fitted"] + sigma = _calculate_sigma(residuals, len(y) - self.season_length) + sigmah = sigma * np.sqrt(k + 1) + pred_int = _calculate_intervals(out, level, h, sigmah) + res = {**res, **pred_int} if fitted: + k = np.floor((h - 1) / self.season_length) + residuals = y - out["fitted"] + sigma = _calculate_sigma(residuals, len(y) - self.season_length) res = _add_fitted_pi(res=res, se=sigma, level=level) return res -# %% ../nbs/src/core/models.ipynb 238 +# %% ../nbs/src/core/models.ipynb 246 @njit def _window_average( y: np.ndarray, # time series @@ -3137,9 +3368,14 @@ def _window_average( mean = _repeat_val(val=wavg, h=h) return {"mean": mean} -# %% ../nbs/src/core/models.ipynb 239 +# %% ../nbs/src/core/models.ipynb 247 class WindowAverage(_TS): - def __init__(self, window_size: int, alias: str = "WindowAverage"): + def __init__( + self, + window_size: int, + alias: str = "WindowAverage", + prediction_intervals: Optional[ConformalIntervals] = None, + ): """WindowAverage model. Uses the average of the last $k$ observations, with $k$ the length of the window. @@ -3156,9 +3392,15 @@ def __init__(self, window_size: int, alias: str = "WindowAverage"): Size of truncated series on which average is estimated. alias : str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ self.window_size = window_size self.alias = alias + self.prediction_intervals = prediction_intervals + self.only_conformal_intervals = True def __repr__(self): return self.alias @@ -3187,12 +3429,14 @@ def fit( """ mod = _window_average(y=y, h=1, window_size=self.window_size, fitted=False) self.model_ = dict(mod) + self._store_cs(y=y, X=X) return self def predict( self, h: int, X: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, ): """Predict with fitted WindowAverage. @@ -3200,6 +3444,10 @@ def predict( ---------- h : int Forecast horizon. + X : numpy.array + Optional exogenous of shape (h, n_x). + level : List[float] + Confidence levels (0-100) for prediction intervals. Returns ------- @@ -3208,6 +3456,13 @@ def predict( """ mean = _repeat_val(self.model_["mean"][0], h=h) res = {"mean": mean} + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: + raise Exception("You must pass `prediction_intervals` to compute them.") return res def predict_in_sample(self): @@ -3231,6 +3486,7 @@ def forecast( h: int, X: Optional[np.ndarray] = None, X_future: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, fitted: bool = False, ): """Memory Efficient WindowAverage predictions. @@ -3259,10 +3515,18 @@ def forecast( forecasts : dict Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions. """ - out = _window_average(y=y, h=h, fitted=fitted, window_size=self.window_size) - return out + res = _window_average(y=y, h=h, fitted=fitted, window_size=self.window_size) + res = dict(res) + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) + else: + raise Exception("You must pass `prediction_intervals` to " "compute them.") + return res -# %% ../nbs/src/core/models.ipynb 249 +# %% ../nbs/src/core/models.ipynb 258 @njit def _seasonal_window_average( y: np.ndarray, @@ -3283,9 +3547,15 @@ def _seasonal_window_average( out = _repeat_val_seas(season_vals=season_avgs, h=h, season_length=season_length) return {"mean": out} -# %% ../nbs/src/core/models.ipynb 250 +# %% ../nbs/src/core/models.ipynb 259 class SeasonalWindowAverage(_TS): - def __init__(self, season_length: int, window_size: int, alias: str = "SeasWA"): + def __init__( + self, + season_length: int, + window_size: int, + alias: str = "SeasWA", + prediction_intervals: Optional[ConformalIntervals] = None, + ): """SeasonalWindowAverage model. An average of the last $k$ observations of the same period, with $k$ the length of the window. @@ -3301,10 +3571,16 @@ def __init__(self, season_length: int, window_size: int, alias: str = "SeasWA"): Number of observations per cycle. alias : str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ self.season_length = season_length self.window_size = window_size self.alias = alias + self.prediction_intervals = prediction_intervals + self.only_conformal_intervals = True def __repr__(self): return self.alias @@ -3339,12 +3615,14 @@ def fit( window_size=self.window_size, ) self.model_ = dict(mod) + self._store_cs(y=y, X=X) return self def predict( self, h: int, X: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, ): """Predict with fitted SeasonalWindowAverage. @@ -3352,6 +3630,10 @@ def predict( ---------- h : int Forecast horizon. + X : array-like + Optional insample exogenous of shape (t, n_x). + level : List[float] + Confidence levels (0-100) for prediction intervals. Returns ------- @@ -3362,6 +3644,13 @@ def predict( season_vals=self.model_["mean"], season_length=self.season_length, h=h ) res = {"mean": mean} + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: + raise Exception("You must pass `prediction_intervals` to compute them.") return res def predict_in_sample(self): @@ -3385,6 +3674,7 @@ def forecast( h: int, X: Optional[np.ndarray] = None, X_future: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, fitted: bool = False, ): """Memory Efficient SeasonalWindowAverage predictions. @@ -3414,16 +3704,24 @@ def forecast( forecasts : dict Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions. """ - out = _seasonal_window_average( + res = _seasonal_window_average( y=y, h=h, fitted=fitted, season_length=self.season_length, window_size=self.window_size, ) - return out + res = dict(res) + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) + else: + raise Exception("You must pass `prediction_intervals` to compute them.") + return res -# %% ../nbs/src/core/models.ipynb 261 +# %% ../nbs/src/core/models.ipynb 271 def _adida( y: np.ndarray, # time series h: int, # forecasting horizon @@ -3444,9 +3742,13 @@ def _adida( mean = _repeat_val(val=forecast, h=h) return {"mean": mean} -# %% ../nbs/src/core/models.ipynb 262 +# %% ../nbs/src/core/models.ipynb 272 class ADIDA(_TS): - def __init__(self, alias: str = "ADIDA"): + def __init__( + self, + alias: str = "ADIDA", + prediction_intervals: Optional[ConformalIntervals] = None, + ): """ADIDA model. Aggregate-Dissagregate Intermittent Demand Approach: Uses temporal aggregation to reduce the @@ -3465,8 +3767,14 @@ def __init__(self, alias: str = "ADIDA"): ---------- alias : str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ self.alias = alias + self.prediction_intervals = prediction_intervals + self.only_conformal_intervals = True def __repr__(self): return self.alias @@ -3492,12 +3800,14 @@ def fit( """ mod = _adida(y=y, h=1, fitted=False) self.model_ = dict(mod) + self._store_cs(y=y, X=X) return self def predict( self, h: int, X: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, ): """Predict with fitted ADIDA. @@ -3505,6 +3815,10 @@ def predict( ---------- h : int Forecast horizon. + X : array-like + Optional exogenous of shape (h, n_x). + level : List[float] + Confidence levels (0-100) for prediction intervals. Returns ------- @@ -3513,9 +3827,21 @@ def predict( """ mean = _repeat_val(val=self.model_["mean"][0], h=h) res = {"mean": mean} + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: + raise Exception( + "You have to instantiate the class with `prediction_intervals`" + "to calculate them" + ) return res - def predict_in_sample(self): + def predict_in_sample( + self, + ): """Access fitted ADIDA insample predictions. Parameters @@ -3536,6 +3862,7 @@ def forecast( h: int, X: Optional[np.ndarray] = None, X_future: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, fitted: bool = False, ): """Memory Efficient ADIDA predictions. @@ -3554,6 +3881,8 @@ def forecast( Optional insample exogenous of shape (t, n_x). X_future : array-like Optional exogenous of shape (h, n_x). + level : List[float] + Confidence levels (0-100) for prediction intervals. fitted : bool Whether or not to return insample predictions. @@ -3562,10 +3891,20 @@ def forecast( forecasts : dict Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions. """ - out = _adida(y=y, h=h, fitted=fitted) - return out + res = _adida(y=y, h=h, fitted=fitted) + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) + else: + raise Exception( + "You have to instantiate the class with `prediction_intervals`" + "to calculate them" + ) + return res -# %% ../nbs/src/core/models.ipynb 273 +# %% ../nbs/src/core/models.ipynb 284 @njit def _croston_classic( y: np.ndarray, # time series @@ -3587,9 +3926,13 @@ def _croston_classic( mean = _repeat_val(val=mean, h=h) return {"mean": mean} -# %% ../nbs/src/core/models.ipynb 274 +# %% ../nbs/src/core/models.ipynb 285 class CrostonClassic(_TS): - def __init__(self, alias: str = "CrostonClassic"): + def __init__( + self, + alias: str = "CrostonClassic", + prediction_intervals: Optional[ConformalIntervals] = None, + ): """CrostonClassic model. A method to forecast time series that exhibit intermittent demand. @@ -3607,8 +3950,14 @@ def __init__(self, alias: str = "CrostonClassic"): ---------- alias : str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ self.alias = alias + self.prediction_intervals = prediction_intervals + self.only_conformal_intervals = True def __repr__(self): return self.alias @@ -3634,12 +3983,14 @@ def fit( """ mod = _croston_classic(y=y, h=1, fitted=False) self.model_ = dict(mod) + self._store_cs(y=y, X=X) return self def predict( self, h: int, X: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, ): """Predict with fitted CrostonClassic. @@ -3647,6 +3998,10 @@ def predict( ---------- h : int Forecast horizon. + X : array-like + Optional exogenous of shape (h, n_x). + level : List[float] + Confidence levels (0-100) for prediction intervals. Returns ------- @@ -3655,6 +4010,15 @@ def predict( """ mean = _repeat_val(val=self.model_["mean"][0], h=h) res = {"mean": mean} + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: + raise Exception( + "You have to instantiate the class with `prediction_intervals` to calculate them" + ) return res def predict_in_sample(self, level): @@ -3678,6 +4042,7 @@ def forecast( h: int, X: Optional[np.ndarray] = None, X_future: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, fitted: bool = False, ): """Memory Efficient CrostonClassic predictions. @@ -3696,6 +4061,8 @@ def forecast( Optional insample exogenous of shape (t, n_x). X_future : array-like Optional exogenous of shape (h, n_x). + level : List[float] + Confidence levels (0-100) for prediction intervals. fitted : bool Whether or not returns insample predictions. @@ -3704,10 +4071,19 @@ def forecast( forecasts : dict Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions. """ - out = _croston_classic(y=y, h=h, fitted=fitted) - return out + res = _croston_classic(y=y, h=h, fitted=fitted) + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=dict(res), y=y, X=X, level=level) + else: + raise Exception( + "You have to instantiate the class with `prediction_intervals` to calculate them" + ) + return res -# %% ../nbs/src/core/models.ipynb 284 +# %% ../nbs/src/core/models.ipynb 296 def _croston_optimized( y: np.ndarray, # time series h: int, # forecasting horizon @@ -3728,9 +4104,13 @@ def _croston_optimized( mean = _repeat_val(val=mean, h=h) return {"mean": mean} -# %% ../nbs/src/core/models.ipynb 285 +# %% ../nbs/src/core/models.ipynb 297 class CrostonOptimized(_TS): - def __init__(self, alias: str = "CrostonOptimized"): + def __init__( + self, + alias: str = "CrostonOptimized", + prediction_intervals: Optional[ConformalIntervals] = None, + ): """CrostonOptimized model. A method to forecast time series that exhibit intermittent demand. @@ -3749,8 +4129,14 @@ def __init__(self, alias: str = "CrostonOptimized"): ---------- alias : str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ self.alias = alias + self.prediction_intervals = prediction_intervals + self.only_conformal_intervals = True def __repr__(self): return self.alias @@ -3776,12 +4162,14 @@ def fit( """ mod = _croston_optimized(y=y, h=1, fitted=False) self.model_ = dict(mod) + self._store_cs(y=y, X=X) return self def predict( self, h: int, X: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, ): """Predict with fitted CrostonOptimized. @@ -3789,6 +4177,10 @@ def predict( ---------- h : int Forecast horizon. + X : array-like + Optional insample exogenous of shape (t, n_x). + level : List[float] + Confidence levels (0-100) for prediction intervals. Returns ------- @@ -3797,6 +4189,13 @@ def predict( """ mean = _repeat_val(val=self.model_["mean"][0], h=h) res = {"mean": mean} + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: + raise Exception("You must pass `prediction_intervals` to compute them.") return res def predict_in_sample(self): @@ -3820,6 +4219,7 @@ def forecast( h: int, X: Optional[np.ndarray] = None, X_future: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, fitted: bool = False, ): """Memory Efficient CrostonOptimized predictions. @@ -3846,10 +4246,18 @@ def forecast( forecasts : dict Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions. """ - out = _croston_optimized(y=y, h=h, fitted=fitted) - return out + res = _croston_optimized(y=y, h=h, fitted=fitted) + res = dict(res) + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) + else: + raise Exception("You must pass `prediction_intervals` to compute them.") + return res -# %% ../nbs/src/core/models.ipynb 295 +# %% ../nbs/src/core/models.ipynb 308 @njit def _croston_sba( y: np.ndarray, # time series @@ -3862,9 +4270,13 @@ def _croston_sba( mean["mean"] *= 0.95 return mean -# %% ../nbs/src/core/models.ipynb 296 +# %% ../nbs/src/core/models.ipynb 309 class CrostonSBA(_TS): - def __init__(self, alias: str = "CrostonSBA"): + def __init__( + self, + alias: str = "CrostonSBA", + prediction_intervals: Optional[ConformalIntervals] = None, + ): """CrostonSBA model. A method to forecast time series that exhibit intermittent demand. @@ -3883,8 +4295,14 @@ def __init__(self, alias: str = "CrostonSBA"): ---------- alias : str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ self.alias = alias + self.prediction_intervals = prediction_intervals + self.only_conformal_intervals = True def __repr__(self): return self.alias @@ -3910,12 +4328,14 @@ def fit( """ mod = _croston_sba(y=y, h=1, fitted=False) self.model_ = dict(mod) + self._store_cs(y=y, X=X) return self def predict( self, h: int, X: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, ): """Predict with fitted CrostonSBA. @@ -3923,6 +4343,10 @@ def predict( ---------- h : int Forecast horizon. + X : array-like + Optional exogenous of shape (h, n_x). + level : List[float] + Confidence levels (0-100) for prediction intervals. Returns ------- @@ -3931,6 +4355,15 @@ def predict( """ mean = _repeat_val(val=self.model_["mean"][0], h=h) res = {"mean": mean} + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: + raise Exception( + "You have to instantiate the class with `prediction_intervals` to calculate them" + ) return res def predict_in_sample(self): @@ -3954,6 +4387,7 @@ def forecast( h: int, X: Optional[np.ndarray] = None, X_future: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, fitted: bool = False, ): """Memory Efficient CrostonSBA predictions. @@ -3972,6 +4406,8 @@ def forecast( Optional insample exogenous of shape (t, n_x). X_future : array-like Optional exogenous of shape (h, n_x). + level : List[float] + Confidence levels (0-100) for prediction intervals. fitted : bool Whether or not to return insample predictions. @@ -3980,10 +4416,20 @@ def forecast( forecasts : dict Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions. """ - out = _croston_sba(y=y, h=h, fitted=fitted) - return out + res = _croston_sba(y=y, h=h, fitted=fitted) + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=dict(res), y=y, X=X, level=level) + else: + raise Exception( + "You have to instantiate the class with `prediction_intervals`" + "to calculate them" + ) + return res -# %% ../nbs/src/core/models.ipynb 306 +# %% ../nbs/src/core/models.ipynb 320 def _imapa( y: np.ndarray, # time series h: int, # forecasting horizon @@ -4007,9 +4453,13 @@ def _imapa( mean = _repeat_val(val=forecast, h=h) return {"mean": mean} -# %% ../nbs/src/core/models.ipynb 307 +# %% ../nbs/src/core/models.ipynb 321 class IMAPA(_TS): - def __init__(self, alias: str = "IMAPA"): + def __init__( + self, + alias: str = "IMAPA", + prediction_intervals: Optional[ConformalIntervals] = None, + ): """IMAPA model. Intermittent Multiple Aggregation Prediction Algorithm: Similar to ADIDA, but instead of @@ -4024,8 +4474,14 @@ def __init__(self, alias: str = "IMAPA"): ---------- alias : str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ self.alias = alias + self.prediction_intervals = prediction_intervals + self.only_conformal_intervals = True def __repr__(self): return self.alias @@ -4051,12 +4507,14 @@ def fit( """ mod = _imapa(y=y, h=1, fitted=False) self.model_ = dict(mod) + self._store_cs(y=y, X=X) return self def predict( self, h: int, X: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, ): """Predict with fitted IMAPA. @@ -4069,9 +4527,23 @@ def predict( ------- forecasts : dict Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions. + X : array-like + Optional exogenous of shape (h, n_x). + level : List[float] + Confidence levels (0-100) for prediction intervals. """ mean = _repeat_val(val=self.model_["mean"][0], h=h) res = {"mean": mean} + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: + raise Exception( + "You have to instantiate the class with `prediction_intervals`" + "to calculate them" + ) return res def predict_in_sample(self): @@ -4095,6 +4567,7 @@ def forecast( h: int, X: Optional[np.ndarray] = None, X_future: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, fitted: bool = False, ): """Memory Efficient IMAPA predictions. @@ -4113,6 +4586,8 @@ def forecast( Optional insample exogenous of shape (t, n_x). X_future : array-like Optional exogenous of shape (h, n_x). + level : List[float] + Confidence levels (0-100) for prediction intervals. fitted : bool Whether or not to return insample predictions. @@ -4121,10 +4596,20 @@ def forecast( forecasts : dict Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions. """ - out = _imapa(y=y, h=h, fitted=fitted) - return out + res = _imapa(y=y, h=h, fitted=fitted) + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) + else: + raise Exception( + "You have to instantiate the class with `prediction_intervals`" + "to calculate them" + ) + return res -# %% ../nbs/src/core/models.ipynb 317 +# %% ../nbs/src/core/models.ipynb 332 @njit def _tsb( y: np.ndarray, # time series @@ -4145,9 +4630,15 @@ def _tsb( mean = _repeat_val(val=forecast, h=h) return {"mean": mean} -# %% ../nbs/src/core/models.ipynb 318 +# %% ../nbs/src/core/models.ipynb 333 class TSB(_TS): - def __init__(self, alpha_d: float, alpha_p: float, alias: str = "TSB"): + def __init__( + self, + alpha_d: float, + alpha_p: float, + alias: str = "TSB", + prediction_intervals: Optional[ConformalIntervals] = None, + ): """TSB model. Teunter-Syntetos-Babai: A modification of Croston's method that replaces the inter-demand @@ -4178,10 +4669,16 @@ def __init__(self, alpha_d: float, alpha_p: float, alias: str = "TSB"): Smoothing parameter for probability. alias : str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ self.alpha_d = alpha_d self.alpha_p = alpha_p self.alias = alias + self.prediction_intervals = prediction_intervals + self.only_conformal_intervals = True def __repr__(self): return self.alias @@ -4207,12 +4704,14 @@ def fit( """ mod = _tsb(y=y, h=1, fitted=False, alpha_d=self.alpha_d, alpha_p=self.alpha_p) self.model_ = dict(mod) + self._store_cs(y=y, X=X) return self def predict( self, h: int, X: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, ): """Predict with fitted TSB. @@ -4220,6 +4719,8 @@ def predict( ---------- h : int Forecast horizon. + level : List[float] + Confidence levels (0-100) for prediction intervals. Returns ------- @@ -4228,6 +4729,13 @@ def predict( """ mean = _repeat_val(self.model_["mean"][0], h=h) res = {"mean": mean} + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: + raise Exception("You must pass `prediction_intervals` to " "compute them.") return res def predict_in_sample(self): @@ -4251,6 +4759,7 @@ def forecast( h: int, X: Optional[np.ndarray] = None, X_future: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, fitted: bool = False, ): """Memory Efficient TSB predictions. @@ -4277,10 +4786,18 @@ def forecast( forecasts : dict Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions. """ - out = _tsb(y=y, h=h, fitted=fitted, alpha_d=self.alpha_d, alpha_p=self.alpha_p) - return out + res = _tsb(y=y, h=h, fitted=fitted, alpha_d=self.alpha_d, alpha_p=self.alpha_p) + res = dict(res) + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) + else: + raise Exception("You must pass `prediction_intervals` to compute them.") + return res -# %% ../nbs/src/core/models.ipynb 329 +# %% ../nbs/src/core/models.ipynb 345 def _predict_mstl_seas(mstl_ob, h, season_length): seasoncolumns = mstl_ob.filter(regex="seasonal*").columns nseasons = len(seasoncolumns) @@ -4297,7 +4814,7 @@ def _predict_mstl_seas(mstl_ob, h, season_length): lastseas = seascomp.sum(axis=1) return lastseas -# %% ../nbs/src/core/models.ipynb 330 +# %% ../nbs/src/core/models.ipynb 346 class MSTL(_TS): """MSTL model. @@ -4319,6 +4836,10 @@ class MSTL(_TS): The `period` and `seasonal` arguments are reserved. alias : str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ def __init__( @@ -4327,6 +4848,7 @@ def __init__( trend_forecaster=AutoETS(model="ZZN"), stl_kwargs: Optional[Dict] = None, alias: str = "MSTL", + prediction_intervals: Optional[ConformalIntervals] = None, ): # check ETS model doesnt have seasonality if repr(trend_forecaster) == "AutoETS": @@ -4344,7 +4866,13 @@ def __init__( ) self.season_length = season_length self.trend_forecaster = trend_forecaster + self.prediction_intervals = prediction_intervals self.alias = alias + + if self.trend_forecaster.prediction_intervals is None and ( + self.prediction_intervals is not None + ): + self.trend_forecaster.prediction_intervals = prediction_intervals self.stl_kwargs = dict() if stl_kwargs is None else stl_kwargs def __repr__(self): @@ -4378,13 +4906,14 @@ def fit( ) x_sa = self.model_[["trend", "remainder"]].sum(axis=1).values self.trend_forecaster = self.trend_forecaster.new().fit(y=x_sa, X=X) + self._store_cs(y=y, X=X) return self def predict( self, h: int, X: Optional[np.ndarray] = None, - level: Optional[Tuple[int]] = None, + level: Optional[List[int]] = None, ): """Predict with fitted MSTL. @@ -4394,7 +4923,7 @@ def predict( Forecast horizon. X : array-like Optional exogenous of shape (h, n_x). - level : List[floar] + level : List[float] Confidence levels (0-100) for prediction intervals. Returns @@ -4408,9 +4937,18 @@ def predict( res = self.trend_forecaster.predict(**kwargs) seas = _predict_mstl_seas(self.model_, h=h, season_length=self.season_length) res = {key: val + seas for key, val in res.items()} + if level is None: + return res + level = sorted(level) + if self.trend_forecaster.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: + raise Exception( + "You have to instantiate either the trend forecaster class or MSTL class with `prediction_intervals` to calculate them" + ) return res - def predict_in_sample(self, level: Optional[Tuple[int]] = None): + def predict_in_sample(self, level: Optional[List[int]] = None): """Access fitted MSTL insample predictions. Parameters @@ -4484,6 +5022,15 @@ def forecast( key: val + (seas_insample if "fitted" in key else seas_h) for key, val in res.items() } + if level is None: + return res + level = sorted(level) + if self.trend_forecaster.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) + else: + raise Exception( + "You have to instantiate either the trend forecaster class or MSTL class with `prediction_intervals` to calculate them" + ) return res def forward( @@ -4536,9 +5083,13 @@ def forward( key: val + (seas_insample if "fitted" in key else seas_h) for key, val in res.items() } + if level is not None: + level = sorted(level) + if self.trend_forecaster.prediction_intervals is not None: + res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level) return res -# %% ../nbs/src/core/models.ipynb 343 +# %% ../nbs/src/core/models.ipynb 361 class Theta(AutoTheta): """Standard Theta Method. @@ -4574,7 +5125,7 @@ def __init__( prediction_intervals=prediction_intervals, ) -# %% ../nbs/src/core/models.ipynb 356 +# %% ../nbs/src/core/models.ipynb 374 class OptimizedTheta(AutoTheta): """Optimized Theta Method. @@ -4610,7 +5161,7 @@ def __init__( prediction_intervals=prediction_intervals, ) -# %% ../nbs/src/core/models.ipynb 369 +# %% ../nbs/src/core/models.ipynb 387 class DynamicTheta(AutoTheta): """Dynamic Standard Theta Method. @@ -4646,7 +5197,7 @@ def __init__( prediction_intervals=prediction_intervals, ) -# %% ../nbs/src/core/models.ipynb 382 +# %% ../nbs/src/core/models.ipynb 400 class DynamicOptimizedTheta(AutoTheta): """Dynamic Optimized Theta Method. @@ -4682,7 +5233,7 @@ def __init__( prediction_intervals=prediction_intervals, ) -# %% ../nbs/src/core/models.ipynb 396 +# %% ../nbs/src/core/models.ipynb 414 class GARCH(_TS): """Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. @@ -4718,15 +5269,26 @@ class GARCH(_TS): Number of lagged versions of the volatility. alias : str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ - def __init__(self, p: int = 1, q: int = 1, alias: str = "GARCH"): + def __init__( + self, + p: int = 1, + q: int = 1, + alias: str = "GARCH", + prediction_intervals: Optional[ConformalIntervals] = None, + ): self.p = p self.q = q if q != 0: self.alias = alias + "(" + str(p) + "," + str(q) + ")" else: self.alias = alias + "(" + str(p) + ")" + self.prediction_intervals = prediction_intervals def __repr__(self): return self.alias @@ -4746,9 +5308,9 @@ def fit(self, y: np.ndarray, X: Optional[np.ndarray] = None): self : GARCH model. """ - self.model_ = garch_model(y, p=self.p, q=self.q) self.model_["actual_residuals"] = y - self.model_["fitted"] + self._store_cs(y, X) return self def predict( @@ -4760,6 +5322,8 @@ def predict( ---------- h : int Forecast horizon. + X : array-like + Optional exogenous of shape (h, n_x). level : List[float] Confidence levels (0-100) for prediction intervals. @@ -4770,8 +5334,12 @@ def predict( """ fcst = garch_forecast(self.model_, h) res = {"mean": fcst["mean"], "sigma2": fcst["sigma2"]} - if level is not None: - level = sorted(level) + if level is None: + return res + level = sorted(level) + if self.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: quantiles = _quantiles(level) lo = res["mean"].reshape(-1, 1) - quantiles * res["sigma2"].reshape(-1, 1) hi = res["mean"].reshape(-1, 1) + quantiles * res["sigma2"].reshape(-1, 1) @@ -4781,7 +5349,7 @@ def predict( res = {**res, **lo, **hi} return res - def predict_in_sample(self, level: Optional[Tuple[int]] = None): + def predict_in_sample(self, level: Optional[List[int]] = None): """Access fitted GARCH model predictions. Parameters @@ -4840,19 +5408,26 @@ def forecast( res = {key: fcst[key] for key in keys} if level is not None: level = sorted(level) - quantiles = _quantiles(level) - lo = res["mean"].reshape(-1, 1) - quantiles * res["sigma2"].reshape(-1, 1) - hi = res["mean"].reshape(-1, 1) + quantiles * res["sigma2"].reshape(-1, 1) - lo = lo[:, ::-1] - lo = {f"lo-{l}": lo[:, i] for i, l in enumerate(reversed(level))} - hi = {f"hi-{l}": hi[:, i] for i, l in enumerate(level)} - res = {**res, **lo, **hi} + if self.prediction_intervals is not None: + res = self._add_predict_conformal_intervals(res, level) + else: + quantiles = _quantiles(level) + lo = res["mean"].reshape(-1, 1) - quantiles * res["sigma2"].reshape( + -1, 1 + ) + hi = res["mean"].reshape(-1, 1) + quantiles * res["sigma2"].reshape( + -1, 1 + ) + lo = lo[:, ::-1] + lo = {f"lo-{l}": lo[:, i] for i, l in enumerate(reversed(level))} + hi = {f"hi-{l}": hi[:, i] for i, l in enumerate(level)} + res = {**res, **lo, **hi} if fitted: se = _calculate_sigma(y - mod["fitted"], len(y) - 1) res = _add_fitted_pi(res=res, se=se, level=level) return res -# %% ../nbs/src/core/models.ipynb 408 +# %% ../nbs/src/core/models.ipynb 427 class ARCH(GARCH): """Autoregressive Conditional Heteroskedasticity (ARCH) model. @@ -4879,12 +5454,198 @@ class ARCH(GARCH): Number of lagged versions of the series. alias : str Custom name of the model. + prediction_intervals : Optional[ConformalIntervals] + Information to compute conformal prediction intervals. + By default, the model will compute the native prediction + intervals. """ - def __init__(self, p: int = 1, alias: str = "ARCH"): + def __init__( + self, + p: int = 1, + alias: str = "ARCH", + prediction_intervals: Optional[ConformalIntervals] = None, + ): self.p = p self.alias = alias super().__init__(p, q=0, alias=alias) def __repr__(self): return self.alias + +# %% ../nbs/src/core/models.ipynb 438 +class ConstantModel(_TS): + def __init__(self, constant: float, alias: str = "ConstantModel"): + """Constant Model. + + Returns Constant values. + + Parameters + ---------- + constant: float + Custom value to return as forecast. + alias: str + Custom name of the model. + """ + self.constant = constant + self.alias = alias + + def __repr__(self): + return self.alias + + def fit( + self, + y: np.ndarray, + X: Optional[np.ndarray] = None, + ): + """Fit the Constant model. + + Fit an Constant Model to a time series (numpy.array) `y`. + + Parameters + ---------- + y : numpy.array + Clean time series of shape (t, ). + X : array-like + Optional exogenous of shape (t, n_x). + + Returns + ------- + self: + Constant fitted model. + """ + self.n_y = len(y) + return self + + def predict( + self, + h: int, # forecasting horizon + X: Optional[np.ndarray] = None, # exogenous regressors + level: Optional[List[int]] = None, # confidence level + ): + """Predict with fitted ConstantModel. + + Parameters + ---------- + h : int + Forecast horizon. + X : array-like + Optional exogenous of shape (h, n_x). + level : List[float] + Confidence levels (0-100) for prediction intervals. + + Returns + ------- + forecasts : dict + Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions. + """ + mean = np.full(h, self.constant, dtype=np.float32) + res = {"mean": mean} + + if level is not None: + for lv in sorted(level): + res[f"lo-{lv}"] = mean + res[f"hi-{lv}"] = mean + + return res + + def predict_in_sample(self, level: Optional[List[int]] = None): + """Access fitted Constant Model insample predictions. + + Parameters + ---------- + level : List[float] + Confidence levels (0-100) for prediction intervals. + + Returns + ------- + forecasts : dict + Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions. + """ + fitted = np.full(self.n_y, self.constant, dtype=np.float32) + res = {"fitted": fitted} + if level is not None: + for lv in sorted(level): + res[f"fitted-lo-{lv}"] = fitted + res[f"fitted-hi-{lv}"] = fitted + + return res + + def forecast( + self, + y: np.ndarray, + h: int, + X: Optional[np.ndarray] = None, + X_future: Optional[np.ndarray] = None, + level: Optional[List[int]] = None, + fitted: bool = False, + ): + """Memory Efficient Constant Model predictions. + + This method avoids memory burden due from object storage. + It is analogous to `fit_predict` without storing information. + It assumes you know the forecast horizon in advance. + + Parameters + ---------- + y : numpy.array + Clean time series of shape (n,). + h: int + Forecast horizon. + X : array-like + Optional insample exogenous of shape (t, n_x). + X_future : array-like + Optional exogenous of shape (h, n_x). + level : List[float] + Confidence levels (0-100) for prediction intervals. + fitted : bool + Whether or not to return insample predictions. + + Returns + ------- + forecasts : dict + Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions. + """ + mean = np.full(h, self.constant, dtype=np.float32) + res = {"mean": mean} + + if fitted: + fitted_vals = np.full(self.n_y, self.constant, dtype=np.float32) + res["fitted"] = fitted_vals + + if level is not None: + for lv in sorted(level): + res[f"lo-{lv}"] = mean + res[f"hi-{lv}"] = mean + if fitted: + res[f"fitted-lo-{lv}"] = fitted_vals + res[f"fitted-hi-{lv}"] = fitted_vals + return res + +# %% ../nbs/src/core/models.ipynb 449 +class ZeroModel(ConstantModel): + def __init__(self, alias: str = "ZeroModel"): + """Returns Zero forecasts. + + Returns Zero values. + + Parameters + ---------- + alias: str + Custom name of the model. + """ + super().__init__(constant=0, alias=alias) + +# %% ../nbs/src/core/models.ipynb 460 +class NaNModel(ConstantModel): + def __init__(self, alias: str = "NaNModel"): + """NaN Model. + + Returns NaN values. + + Parameters + ---------- + alias: str + Custom name of the model. + """ + super().__init__(constant=np.nan, alias=alias)