Skip to content

make_exogenous_regression

yohou.datasets._generators.make_exogenous_regression(*, n_samples=200, forecasting_horizon=6, noise=0.1, forecast_bias=0.5, random_state=42)

Generate a synthetic regression dataset with exogenous features.

Creates hourly electricity prices driven by temperature and a holiday indicator with a known linear relationship: price = 50 + 2 * temperature + 10 * is_holiday + noise.

Three exogenous feature types are produced:

  • X_actual (observation features): realized temperature readings with a 24 hour sinusoidal cycle.
  • X_future (known future): a deterministic is_holiday indicator (Sundays = 1.0) covering the full time range.
  • X_forecast (external forecasts): weather temperature forecasts with one vintage per observation, each covering the next forecasting_horizon steps. Forecasts carry a small systematic bias relative to actuals.

Parameters

Name Type Description Default
n_samples int

Number of hourly observations.

 200
forecasting_horizon int

Number of forward steps per X_forecast vintage.

 6
noise float

Standard deviation of the target noise term.

 0.1
forecast_bias float

Systematic bias added to weather forecasts relative to actuals.

 0.5
random_state int

Seed for reproducibility.

 42

Returns

Type Description
Bunch

Dictionary-like object with the following attributes:

y : pl.DataFrame Target with columns ["time", "price"]. X_actual : pl.DataFrame Observation features with columns ["time", "temperature"]. X_future : pl.DataFrame Known future features with columns ["time", "is_holiday"]. X_forecast : pl.DataFrame External forecasts with columns ["vintage_time", "time", "wx_temp"]. One vintage per observation from index forecasting_horizon onward. frame : pl.DataFrame y, X_actual, and X_future joined on "time". X_forecast is excluded because it has a different schema. feature_names : list of str ["temperature", "is_holiday", "wx_temp"]. target_names : list of str ["price"]. frequency : str "1h". DESCR : str Human readable description.

See Also

Examples

>>> from yohou.datasets import make_exogenous_regression
>>> data = make_exogenous_regression(n_samples=100)
>>> data.y.columns
['time', 'price']
>>> data.X_forecast.columns
['vintage_time', 'time', 'wx_temp']

Source Code

View on GitHub

Show/Hide source 
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
def make_exogenous_regression(
    *,
    n_samples: int = 200,
    forecasting_horizon: int = 6,
    noise: float = 0.1,
    forecast_bias: float = 0.5,
    random_state: int = 42,
) -> Bunch:
    """Generate a synthetic regression dataset with exogenous features.

    Creates hourly electricity prices driven by temperature and a holiday
    indicator with a known linear relationship:
    ``price = 50 + 2 * temperature + 10 * is_holiday + noise``.

    Three exogenous feature types are produced:

    - **X_actual** (observation features): realized temperature readings
      with a 24 hour sinusoidal cycle.
    - **X_future** (known future): a deterministic ``is_holiday`` indicator
      (Sundays = 1.0) covering the full time range.
    - **X_forecast** (external forecasts): weather temperature forecasts
      with one vintage per observation, each covering the next
      ``forecasting_horizon`` steps. Forecasts carry a small systematic
      bias relative to actuals.

    Parameters
    ----------
    n_samples : int, default=200
        Number of hourly observations.
    forecasting_horizon : int, default=6
        Number of forward steps per X_forecast vintage.
    noise : float, default=0.1
        Standard deviation of the target noise term.
    forecast_bias : float, default=0.5
        Systematic bias added to weather forecasts relative to actuals.
    random_state : int, default=42
        Seed for reproducibility.

    Returns
    -------
    Bunch
        Dictionary-like object with the following attributes:

        y : pl.DataFrame
            Target with columns ``["time", "price"]``.
        X_actual : pl.DataFrame
            Observation features with columns ``["time", "temperature"]``.
        X_future : pl.DataFrame
            Known future features with columns ``["time", "is_holiday"]``.
        X_forecast : pl.DataFrame
            External forecasts with columns
            ``["vintage_time", "time", "wx_temp"]``. One vintage per
            observation from index ``forecasting_horizon`` onward.
        frame : pl.DataFrame
            ``y``, ``X_actual``, and ``X_future`` joined on ``"time"``.
            ``X_forecast`` is excluded because it has a different schema.
        feature_names : list of str
            ``["temperature", "is_holiday", "wx_temp"]``.
        target_names : list of str
            ``["price"]``.
        frequency : str
            ``"1h"``.
        DESCR : str
            Human readable description.

    See Also
    --------
    - [`make_exogenous_classification`][yohou.datasets._generators.make_exogenous_classification] : Classification variant with categorical target.
    - [`fetch_tourism_monthly`][yohou.datasets._fetchers.fetch_tourism_monthly] : Real monthly tourism dataset (univariate).

    Examples
    --------
    >>> from yohou.datasets import make_exogenous_regression
    >>> data = make_exogenous_regression(n_samples=100)
    >>> data.y.columns
    ['time', 'price']
    >>> data.X_forecast.columns
    ['vintage_time', 'time', 'wx_temp']

    """
    rng = np.random.default_rng(random_state)
    times = pl.Series(
        "time",
        [datetime(2024, 1, 1) + timedelta(hours=i) for i in range(n_samples)],
    )
    t = np.arange(n_samples, dtype=float)

    actual_temp = 15.0 + 5.0 * np.sin(2 * np.pi * t / 24) + rng.normal(0, 0.5, n_samples)
    holidays = np.array([
        1.0 if (datetime(2024, 1, 1) + timedelta(hours=i)).weekday() == 6 else 0.0 for i in range(n_samples)
    ])
    price = 50.0 + 2.0 * actual_temp + 10.0 * holidays + rng.normal(0, noise, n_samples)

    y = pl.DataFrame({"time": times, "price": price})
    X_actual = pl.DataFrame({"time": times, "temperature": actual_temp})
    X_future = pl.DataFrame({"time": times, "is_holiday": holidays})

    forecast_rows: list[dict[str, object]] = []
    for i in range(forecasting_horizon, n_samples):
        for step in range(1, forecasting_horizon + 1):
            if i + step < n_samples:
                forecast_rows.append({
                    "vintage_time": times[i],
                    "time": times[i + step],
                    "wx_temp": float(actual_temp[i + step] + forecast_bias + rng.normal(0, 0.3)),
                })
    X_forecast = pl.DataFrame(forecast_rows)

    frame = y.join(X_actual, on="time").join(X_future, on="time")

    return Bunch(
        y=y,
        X_actual=X_actual,
        X_future=X_future,
        X_forecast=X_forecast,
        frame=frame,
        feature_names=["temperature", "is_holiday", "wx_temp"],
        target_names=["price"],
        frequency="1h",
        DESCR=(
            "Synthetic hourly electricity prices with exogenous features.\n"
            "Target: price = 50 + 2 * temperature + 10 * is_holiday + noise.\n"
            "X_actual: realized temperature (sinusoidal 24h cycle + noise).\n"
            "X_future: is_holiday indicator (Sundays = 1.0).\n"
            "X_forecast: weather temperature forecasts with systematic bias."
        ),
    )

Tutorials

The following example notebooks use this component:

  • How to Align Exogenous Features Across Pipeline Steps

    Data-Features

    Control which step-indexed columns each direct-strategy estimator sees using the step_feature_alignment parameter of PointReductionForecaster.

    View · Open in marimo

  • How to Produce Multi-Vintage Predictions

    Forecasting-Models

    Generate multiple predictions from different weather forecast vintages without refitting, using the X_forecast predict-time override.

    View · Open in marimo

  • Exogenous Features (X_actual, X_future, X_forecast)

    Getting-Started

    Build a forecasting model with actual observations, known-future indicators, and multi-vintage external forecasts on synthetic electricity price data.

    View · Open in marimo