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z*}{0`@DUvSx8Cpwk|COW;YP_@btH7j&LCf7p~?nIiwo4~Zz1MboWA*l-<3EgI+55@dpD*ux{uJfLGgx671vud8+?6z zW8Gb7 + + + + + + + + +nixtlats - Date Features + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + +
    + +
    + + +
    + + + +
    + +
    +
    +

    Date Features

    +
    + + + +
    + + + + +
    + + +
    + + +

    Useful classes to generate date features and add them to TimeGPT.

    +
    +
    +

    CountryHolidays

    +
    +
     CountryHolidays (countries:List[str])
    +
    +

    Given a list of countries, returns a dataframe with holidays for each country.

    +
    +
    c_holidays = CountryHolidays(countries=['US', 'MX'])
    +periods = 365 * 5
    +dates = pd.date_range(end='2023-09-01', periods=periods)
    +holidays_df = c_holidays(dates)
    +holidays_df.head()
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    US_New Year's DayUS_Martin Luther King Jr. DayUS_Washington's BirthdayUS_Memorial DayUS_Independence DayUS_Labor DayUS_Columbus DayUS_Veterans DayUS_Veterans Day (Observed)US_Thanksgiving...MX_Día de la Independencia [Independence Day]MX_Día de la Independencia [Independence Day] (Observed)MX_Día de la Revolución [Revolution Day] (Observed)MX_Día de la Revolución [Revolution Day]MX_Transmisión del Poder Ejecutivo Federal [Change of Federal Government]MX_Transmisión del Poder Ejecutivo Federal [Change of Federal Government] (Observed)MX_Navidad [Christmas]MX_Día de la Constitución [Constitution Day]MX_Año Nuevo [New Year's Day] (Observed)MX_Día del Trabajo [Labour Day] (Observed)
    2018-09-030000010000...0000000000
    2018-09-040000000000...0000000000
    2018-09-050000000000...0000000000
    2018-09-060000000000...0000000000
    2018-09-070000000000...0000000000
    + +

    5 rows × 31 columns

    +
    +
    +
    +
    +
    +
    +

    SpecialDates

    +
    +
     SpecialDates (special_dates:Dict[str,List[str]])
    +
    +

    Given a dictionary of categories and dates, returns a dataframe with the special dates.

    +
    +
    special_dates = SpecialDates(
    +    special_dates={
    +        'Important Dates': ['2021-02-26', '2020-02-26'],
    +        'Very Important Dates': ['2021-01-26', '2020-01-26', '2019-01-26']
    +    }
    +)
    +periods = 365 * 5
    +dates = pd.date_range(end='2023-09-01', periods=periods)
    +holidays_df = special_dates(dates)
    +holidays_df.head()
    +
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    Important DatesVery Important Dates
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b/docs/getting-started/getting_started_short.html new file mode 100644 index 000000000..dfd80b3f2 --- /dev/null +++ b/docs/getting-started/getting_started_short.html @@ -0,0 +1,948 @@ + + + + + + + + + + +nixtlats - TimeGPT Quickstart + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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    TimeGPT Quickstart

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    + Unlock the power of accurate predictions and confidently navigate uncertainty. Reduce uncertainty and resource limitations. With TimeGPT, you can effortlessly access state-of-the-art models to make data-driven decisions. Whether you’re a bank forecasting market trends or a startup predicting product demand, TimeGPT democratizes access to cutting-edge predictive insights. +
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    Introduction

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    Nixtla’s TimeGPT is a generative pre-trained forecasting model for time series data. TimeGPT can produce accurate forecasts for new time series without training, using only historical values as inputs. TimeGPT can be used across a plethora of tasks including demand forecasting, anomaly detection, financial forecasting, and more.

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    The TimeGPT model “reads” time series data much like the way humans read a sentence – from left to right. It looks at windows of past data, which we can think of as “tokens”, and predicts what comes next. This prediction is based on patterns the model identifies in past data and extrapolates into the future.

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    The API provides an interface to TimeGPT, allowing users to leverage its forecasting capabilities to predict future events. TimeGPT can also be used for other time series-related tasks, such as what-if scenarios, anomaly detection, and more.

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    Usage

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    import os
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    +from nixtlats import TimeGPT
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    You can instantiate the TimeGPT class providing your credentials.

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    timegpt = TimeGPT(token=os.environ['TIMEGPT_TOKEN'])
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    You can test the validate of your token calling the validate_token method:

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    timegpt.validate_token()
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    INFO:nixtlats.timegpt:Happy Forecasting! :), If you have questions or need support, please email ops@nixtla.io
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    True
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    Now you can start making forecasts! Let’s import an example on the classic AirPassengers dataset. This dataset contains the monthly number of airline passengers in Australia between 1949 and 1960. First, let’s load the dataset and plot it:

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    import pandas as pd
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    df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv')
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    timestampvalue
    01949-01-01112
    11949-02-01118
    21949-03-01132
    31949-04-01129
    41949-05-01121
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    timegpt.plot(df, time_col='timestamp', target_col='value')
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    • Make sure the target variable column does not have missing or non-numeric values.
    • +
    • Do not include gaps/jumps in the datestamps (for the given frequency) between the first and late datestamps. The forecast function will not impute missing dates.
    • +
    • The format of the datestamp column should be readable by Pandas (see this link for more details).
    • +
    +
    +
    +
    +

    Next, forecast the next 12 months using the SDK forecast method. Set the following parameters:

    +
      +
    • df: A pandas dataframe containing the time series data.
    • +
    • h: The number of steps ahead to forecast.
    • +
    • freq: The frequency of the time series in Pandas format. See pandas’ available frequencies.
    • +
    • time_col: Column that identifies the datestamp column.
    • +
    • target_col: The variable that we want to forecast.
    • +
    +
    +
    timegpt_fcst_df = timegpt.forecast(df=df, h=12, freq='MS', time_col='timestamp', target_col='value')
    +timegpt_fcst_df.head()
    +
    +
    INFO:nixtlats.timegpt:Validating inputs...
    +INFO:nixtlats.timegpt:Preprocessing dataframes...
    +INFO:nixtlats.timegpt:Calling Forecast Endpoint...
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    timestampTimeGPT
    01961-01-01437.837921
    11961-02-01426.062714
    21961-03-01463.116547
    31961-04-01478.244507
    41961-05-01505.646484
    + +
    +
    +
    +
    +
    timegpt.plot(df, timegpt_fcst_df, time_col='timestamp', target_col='value')
    +
    +

    +
    +
    +

    You can also produce a longer forecasts increasing the horizon parameter. For example, let’s forecast the next 36 months:

    +
    +
    timegpt_fcst_df = timegpt.forecast(df=df, h=36, time_col='timestamp', target_col='value', freq='MS')
    +timegpt_fcst_df.head()
    +
    +
    INFO:nixtlats.timegpt:Validating inputs...
    +INFO:nixtlats.timegpt:Preprocessing dataframes...
    +INFO:nixtlats.timegpt:Calling Forecast Endpoint...
    +WARNING:nixtlats.timegpt:The specified horizon "h" exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    timestampTimeGPT
    01961-01-01437.837921
    11961-02-01426.062714
    21961-03-01463.116547
    31961-04-01478.244507
    41961-05-01505.646484
    + +
    +
    +
    +
    +
    timegpt.plot(df, timegpt_fcst_df, time_col='timestamp', target_col='value')
    +
    +

    +
    +
    +

    Or a shorter one:

    +
    +
    timegpt_fcst_df = timegpt.forecast(df=df, h=6, time_col='timestamp', target_col='value', freq='MS')
    +timegpt.plot(df, timegpt_fcst_df, time_col='timestamp', target_col='value')
    +
    +
    INFO:nixtlats.timegpt:Validating inputs...
    +INFO:nixtlats.timegpt:Preprocessing dataframes...
    +INFO:nixtlats.timegpt:Calling Forecast Endpoint...
    +
    +
    +

    +
    +
    +
    +
    +
    + +
    +
    +Warning +
    +
    +
    +

    TimeGPT-1 is currently optimized for short horizon forecasting. While the forecast mehtod will allow any positive and large horizon, the accuracy of the forecasts might degrade. We are currently working to improve the accuracy on longer forecasts.

    +
    +
    +
    +
    +

    Using DateTime index to infer frequency

    +

    The freq parameter, which indicates the time unit between consecutive data points, is particularly critical. Fortunately, you can pass a DataFrame with a DateTime index to the forecasting method, ensuring that your time series data is equipped with necessary temporal features. By assigning a suitable freq parameter to the DateTime index of a DataFrame, you inform the model about the consistent interval between observations — be it days (‘D’), months (‘M’), or another suitable frequency.

    +
    +
    df_time_index = df.set_index('timestamp')
    +df_time_index.index = pd.DatetimeIndex(df_time_index.index, freq='MS')
    +timegpt.forecast(df=df, h=36, time_col='timestamp', target_col='value').head()
    +
    +
    INFO:nixtlats.timegpt:Validating inputs...
    +INFO:nixtlats.timegpt:Inferred freq: MS
    +INFO:nixtlats.timegpt:Preprocessing dataframes...
    +INFO:nixtlats.timegpt:Calling Forecast Endpoint...
    +WARNING:nixtlats.timegpt:The specified horizon "h" exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    timestampTimeGPT
    01961-01-01437.837921
    11961-02-01426.062714
    21961-03-01463.116547
    31961-04-01478.244507
    41961-05-01505.646484
    + +
    +
    +
    + + +
    + +

    Give us a ⭐ on Github

    + +
    + + + + \ No newline at end of file diff --git a/docs/how-to-guides/distributed.spark.html b/docs/how-to-guides/distributed.spark.html new file mode 100644 index 000000000..5d167b658 --- /dev/null +++ b/docs/how-to-guides/distributed.spark.html @@ -0,0 +1,768 @@ + + + + + + + + + + +nixtlats - How to use TimeGPT on Spark + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + +
    + +
    + + +
    + + + +
    + +
    +
    +

    How to use TimeGPT on Spark

    +
    + +
    +
    + Run TimeGPT distributedly on top of Spark. +
    +
    + + +
    + + + + +
    + + +
    + + +
    +

    Installation

    +

    As long as Spark is installed and configured, TimeGPT will be able to use it. If executing on a distributed Spark cluster, make use the nixtlats library is installed across all the workers.

    +
    +

    Executing on Spark

    +

    To run the forecasts distributed on Spark, just pass in a Spark DataFrame instead.

    +

    Instantiate TimeGPT class.

    +
    +
    from nixtlats import TimeGPT
    +
    +timegpt = TimeGPT(token=os.environ['TIMEGPT_TOKEN'])
    +
    +

    Use Spark as an engine.

    +
    +
    from pyspark.sql import SparkSession
    +
    +spark = SparkSession.builder.getOrCreate()
    +
    +
    Setting default log level to "WARN".
    +To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
    +23/11/07 07:00:55 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
    +
    +
    +
    +

    Forecast

    +
    +
    url_df = 'https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short.csv'
    +spark_df = spark.createDataFrame(pd.read_csv(url_df))
    +spark_df.show(5)
    +
    +
                                                                                    
    +
    +
    +
    +---------+-------------------+-----+
    +|unique_id|                 ds|    y|
    ++---------+-------------------+-----+
    +|       BE|2016-12-01 00:00:00| 72.0|
    +|       BE|2016-12-01 01:00:00| 65.8|
    +|       BE|2016-12-01 02:00:00|59.99|
    +|       BE|2016-12-01 03:00:00|50.69|
    +|       BE|2016-12-01 04:00:00|52.58|
    ++---------+-------------------+-----+
    +only showing top 5 rows
    +
    +
    +
    +
    +
    fcst_df = timegpt.forecast(spark_df, h=12).show(5)
    +
    +
    INFO:nixtlats.timegpt:Validating inputs...                        (4 + 16) / 20]
    +INFO:nixtlats.timegpt:Preprocessing dataframes...
    +INFO:nixtlats.timegpt:Inferred freq: H
    +INFO:nixtlats.timegpt:Calling Forecast Endpoint...=============>  (19 + 1) / 20]
    +                                                                                
    +
    +
    +
    +---------+-------------------+------------------+
    +|unique_id|                 ds|           TimeGPT|
    ++---------+-------------------+------------------+
    +|       FR|2016-12-31 00:00:00|62.130218505859375|
    +|       FR|2016-12-31 01:00:00|56.890830993652344|
    +|       FR|2016-12-31 02:00:00| 52.23155212402344|
    +|       FR|2016-12-31 03:00:00| 48.88866424560547|
    +|       FR|2016-12-31 04:00:00| 46.49836730957031|
    ++---------+-------------------+------------------+
    +only showing top 5 rows
    +
    +
    +
    +
    +
    +

    Forecast with exogenous variables

    +

    Exogenous variables or external factors are crucial in time series forecasting as they provide additional information that might influence the prediction. These variables could include holiday markers, marketing spending, weather data, or any other external data that correlate with the time series data you are forecasting.

    +

    For example, if you’re forecasting ice cream sales, temperature data could serve as a useful exogenous variable. On hotter days, ice cream sales may increase.

    +

    To incorporate exogenous variables in TimeGPT, you’ll need to pair each point in your time series data with the corresponding external data.

    +

    Let’s see an example.

    +
    +
    df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short-with-ex-vars.csv')
    +spark_df = spark.createDataFrame(df)
    +spark_df.show(5)
    +
    +
    +---------+-------------------+-----+----------+----------+-----+-----+-----+-----+-----+-----+-----+
    +|unique_id|                 ds|    y|Exogenous1|Exogenous2|day_0|day_1|day_2|day_3|day_4|day_5|day_6|
    ++---------+-------------------+-----+----------+----------+-----+-----+-----+-----+-----+-----+-----+
    +|       BE|2016-12-01 00:00:00| 72.0|   61507.0|   71066.0|  0.0|  0.0|  0.0|  1.0|  0.0|  0.0|  0.0|
    +|       BE|2016-12-01 01:00:00| 65.8|   59528.0|   67311.0|  0.0|  0.0|  0.0|  1.0|  0.0|  0.0|  0.0|
    +|       BE|2016-12-01 02:00:00|59.99|   58812.0|   67470.0|  0.0|  0.0|  0.0|  1.0|  0.0|  0.0|  0.0|
    +|       BE|2016-12-01 03:00:00|50.69|   57676.0|   64529.0|  0.0|  0.0|  0.0|  1.0|  0.0|  0.0|  0.0|
    +|       BE|2016-12-01 04:00:00|52.58|   56804.0|   62773.0|  0.0|  0.0|  0.0|  1.0|  0.0|  0.0|  0.0|
    ++---------+-------------------+-----+----------+----------+-----+-----+-----+-----+-----+-----+-----+
    +only showing top 5 rows
    +
    +
    +
    +

    To produce forecasts we have to add the future values of the exogenous variables. Let’s read this dataset. In this case we want to predict 24 steps ahead, therefore each unique id will have 24 observations.

    +
    +
    future_ex_vars_df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short-future-ex-vars.csv')
    +spark_future_ex_vars_df = spark.createDataFrame(future_ex_vars_df)
    +spark_future_ex_vars_df.show(5)
    +
    +
    +---------+-------------------+----------+----------+-----+-----+-----+-----+-----+-----+-----+
    +|unique_id|                 ds|Exogenous1|Exogenous2|day_0|day_1|day_2|day_3|day_4|day_5|day_6|
    ++---------+-------------------+----------+----------+-----+-----+-----+-----+-----+-----+-----+
    +|       BE|2016-12-31 00:00:00|   64108.0|   70318.0|  0.0|  0.0|  0.0|  0.0|  0.0|  1.0|  0.0|
    +|       BE|2016-12-31 01:00:00|   62492.0|   67898.0|  0.0|  0.0|  0.0|  0.0|  0.0|  1.0|  0.0|
    +|       BE|2016-12-31 02:00:00|   61571.0|   68379.0|  0.0|  0.0|  0.0|  0.0|  0.0|  1.0|  0.0|
    +|       BE|2016-12-31 03:00:00|   60381.0|   64972.0|  0.0|  0.0|  0.0|  0.0|  0.0|  1.0|  0.0|
    +|       BE|2016-12-31 04:00:00|   60298.0|   62900.0|  0.0|  0.0|  0.0|  0.0|  0.0|  1.0|  0.0|
    ++---------+-------------------+----------+----------+-----+-----+-----+-----+-----+-----+-----+
    +only showing top 5 rows
    +
    +
    +
    +

    Let’s call the forecast method, adding this information:

    +
    +
    timegpt_fcst_ex_vars_df = timegpt.forecast(df=spark_df, X_df=spark_future_ex_vars_df, h=24, level=[80, 90])
    +timegpt_fcst_ex_vars_df.show(5)
    +
    +
    INFO:nixtlats.timegpt:Validating inputs...                                      
    +INFO:nixtlats.timegpt:Preprocessing dataframes...
    +INFO:nixtlats.timegpt:Calling Forecast Endpoint...=============>  (19 + 1) / 20]
    +                                                                                
    +
    +
    +
    +---------+-------------------+------------------+------------------+-----------------+-----------------+------------------+
    +|unique_id|                 ds|           TimeGPT|     TimeGPT-lo-90|    TimeGPT-lo-80|    TimeGPT-hi-80|     TimeGPT-hi-90|
    ++---------+-------------------+------------------+------------------+-----------------+-----------------+------------------+
    +|       FR|2016-12-31 00:00:00| 64.97691027939692|60.056473801735784|61.71575274765864|68.23806781113521| 69.89734675705805|
    +|       FR|2016-12-31 01:00:00| 60.14365519077404| 56.12626745731457|56.73784790927991|63.54946247226818| 64.16104292423351|
    +|       FR|2016-12-31 02:00:00| 59.42375860682185| 54.84932824030574|56.52975776758845|62.31775944605525| 63.99818897333796|
    +|       FR|2016-12-31 03:00:00| 55.11264928302748| 47.59671153125746|51.95117842731459|58.27412013874037|  62.6285870347975|
    +|       FR|2016-12-31 04:00:00|54.400922806813526|44.925772896840385|49.65213255412798|59.14971305949907|63.876072716786666|
    ++---------+-------------------+------------------+------------------+-----------------+-----------------+------------------+
    +only showing top 5 rows
    +
    +
    +
    + + +
    +
    +
    + +

    Give us a ⭐ on Github

    + +
    + + + + \ No newline at end of file diff --git a/docs/tutorials/0_anomaly_detection_files/figure-html/cell-10-output-2.png b/docs/tutorials/0_anomaly_detection_files/figure-html/cell-10-output-2.png new file mode 100644 index 0000000000000000000000000000000000000000..0cde49d7aaf2a5ea6caa50b45b312acb5d357d99 GIT binary patch literal 356552 zcmd43bzD?m_dj|DZ~*C$?vRiM>6C5|q@*P!r8|{IkQ7i-1f@ejau7+S1VyD0q+#fW zyGK9I^L(H0y}$e4eZB5^z3@15&dk|+?G^9!UTaURj+Qdsb*k$S1mUTwDBOi0EI$ZB z55aOVP+n&&|%u*UG~d(zNn&cXsn~cC=>jvGwqDbaNHt5#bTwVsP;C za`zPH<#qY@4Loih_PiWsOQGOPu-#RRJt2s|3iSsol__zAU=XCLAgAy5Vq@Aji2C3{ z207svFc8=?G-D%p2g}Nq*uj`qN0`(^iDT*hPu}`sSq?{zj*L4ecM3?^ZR^Xc(QNKu zv9Pc_-k4{3`b*08$Ima*+exWIDO~+2T=!Eh0;K8uKW4ZEZAq6%tV(YOc3e|UVId^? z??d;?UDjrp!v8u*>Ch!({@0O0^Z&c|h&P^)n};!Al~z`2dFe6TH8aDLBSe0{;oTS8 z^qw$_9xYNP@XPr-5B8noiwx9%9W40~k>H$?d-OVITPCtI%(%{O-YxWuddjtNc7OSW zr&iT!+tp3&+!y&33{0q_aGnQwR$)-4Xl2DXB>D??Dsa24^v#>q+rw;mLBzWfl?+&Z 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