Merge branch 'main' into transformer-explainer

.python-version

@@ -0,0 +1 @@
+3.10.14

dl/notebooks/hierarchical_forecasting_mint.py

@@ -176,6 +176,7 @@ def visualize_s_p(self, w_elements, ax):
 # + id="nmCgX4OWAud0"
 
 
+
 # + [markdown] id="C416Q_Zn7sJD"
 # ## Load data
 #
@@ -469,3 +470,4 @@ def visualize_s_p(self, w_elements, ax):
 reconciliator_mint_cov.transform(ts_pred)[ca_columns].plot(linestyle="--")
 
 # + id="uTOQnpE9Vdt7"
+

dl/notebooks/time-series-data-generation.py

@@ -0,0 +1,98 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: light
+#       format_version: '1.5'
+#       jupytext_version: 1.15.2
+#   kernelspec:
+#     display_name: .venv
+#     language: python
+#     name: python3
+# ---
+
+# # Time Series Data Generation
+
+# +
+import numpy as np
+import pandas as pd
+
+import plotly.express as px
+
+
+# +
+def profile_sin(
+    t: np.ndarray, 
+    lambda_min: float, 
+    lambda_max: float
+) -> np.ndarray:
+    """generate a sin wave profile for 
+    the expected number of visitors 
+    in every 10min for each hour during a day
+
+    :param t: time in minutes
+    :param lambda_min: minimum number of visitors
+    :param lambda_max: maximum number of visitors
+    """
+    amplitude = (lambda_max - lambda_min)
+    t_rescaled = (t - t.min())/t.max() * np.pi
+
+    return amplitude * np.sin(t_rescaled) + lambda_min
+
+
+class KioskVisitors:
+    """generate number of visitors for a kiosk store
+
+    :param daily_profile: expectations of visitors 
+        in every 10min for each hour during a day
+    """
+    def __init__(self, daily_profile: np.ndarray):
+        self.daily_profile = daily_profile
+        self.daily_segments = len(daily_profile)
+
+    def __call__(self, n_days: int) -> pd.DataFrame:
+        """generate number of visitors for n_days
+
+        :param n_days: number of days to generate visitors
+        """
+        visitors = np.concatenate([
+            np.random.poisson(self.daily_profile)
+            for _ in range(n_days)
+        ])
+
+        df = pd.DataFrame({
+            "visitors": visitors,
+            "time": np.arange(len(visitors)),
+            "expectation": np.tile(self.daily_profile, n_days)
+        })
+
+        return df
+
+
+
+# -
+
+# Create a sin profile
+
+# +
+t = np.arange(0, 12 * 60/5, 1)
+
+daily_profile = profile_sin(
+    t, lambda_min=0.5, lambda_max=10
+)
+# -
+
+# Generate a time series data representing the number of visitors to a Kiosk.
+
+kiosk_visitors = KioskVisitors(daily_profile=daily_profile)
+
+df_visitors = kiosk_visitors(n_days=10)
+
+px.line(
+    df_visitors,
+    x="time",
+    y=["visitors", "expectation"],
+)
+
+

dl/notebooks/tree_darts_boosted_tree.py

@@ -1,7 +1,7 @@
 # ---
 # jupyter:
 #   jupytext:
-#     formats: ipynb,py
+#     formats: ipynb,py:light
 #     text_representation:
 #       extension: .py
 #       format_name: light
@@ -301,6 +301,7 @@ def find_linear_trend(
 # + id="HHAeUz4zSs-5"
 
 
+
 # + [markdown] id="76W-UUf4wYGt"
 # ### Metrics
 
@@ -380,3 +381,4 @@ def benchmark_predictions(
 # metric_chart_grid.fig.tight_layout(w_pad=1)
 
 # + id="tsk_WtuCW2JD"
+

mkdocs.yml

@@ -259,6 +259,7 @@ nav:
       - "Forecasting with RNN": notebooks/rnn_timeseries.py
       - "Forecasting with Transformer": notebooks/transformer_timeseries_univariate.py
       - "Forecasting with NeuralODE": notebooks/neuralode_timeseries.py
+      - "Generate Time Series Using Statistics": notebooks/time-series-data-generation.py
     - "Small Yet Powerful Concepts":
       - concepts/index.md
       - "Entropy": concepts/entropy.md