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module mlforecast.utils

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function generate_daily_series

generate_daily_series(
    n_series: int,
    min_length: int = 50,
    max_length: int = 500,
    n_static_features: int = 0,
    equal_ends: bool = False,
    static_as_categorical: bool = True,
    with_trend: bool = False,
    seed: int = 0,
    engine: str = 'pandas'
) → Union[DataFrame, DataFrame]

• n_series (int): Number of series for synthetic panel.
• min_length (int, default=50): Minimum length of synthetic panel’s series.
• max_length (int, default=500): Maximum length of synthetic panel’s series.
• n_static_features (int, default=0): Number of static exogenous variables for synthetic panel’s series.
• equal_ends (bool, default=False): Series should end in the same date stamp ds.
• static_as_categorical (bool, default=True): Static features should have a categorical data type.
• with_trend (bool, default=False): Series should have a (positive) trend.
• seed (int, default=0): Random seed used for generating the data.
• engine (str, default=‘pandas’): Output Dataframe type.

• pandas or polars DataFrame: Synthetic panel with columns [unique_id, ds, y] and exogenous features.

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function generate_prices_for_series

generate_prices_for_series(
    series: DataFrame,
    horizon: int = 7,
    seed: int = 0
) → DataFrame

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class PredictionIntervals

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method __init__

__init__(n_windows: int = 2, h: int = 1, method: str = 'conformal_distribution')