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AutoCESForecaster

yohou_nixtla.stats.AutoCESForecaster

Bases: BaseStatsForecaster

AutoCES (Complex Exponential Smoothing) forecaster via statsforecast.

Automatically selects the best CES model.

Parameters

Name Type Description Default
season_length int

Length of the seasonal period.

 1
model str

CES model specification.

 "Z"
freq str or None

Frequency string. Auto-inferred from data if None.

 None
feature_transformer BaseTransformer or None

Transformer applied to exogenous features before fitting/predicting.

 None
target_transformer BaseTransformer or None

Transformer applied to the target before fitting. Inverse-transformed after predicting to return forecasts in the original scale.

 None
target_as_feature ('transformed', 'raw')

Whether to include target values as additional features.

 "transformed"
**params dict

Additional parameters forwarded to statsforecast.models.AutoCES.

 {}

Attributes

Name Type Description
nixtla_forecaster_ StatsForecast

The fitted Nixtla orchestrator.
instance_ AutoCES

The constructed AutoCES model instance.

See Also

AutoETSForecaster : Automatic ETS model selection.

Examples

>>> from yohou_nixtla.stats import AutoCESForecaster
>>> forecaster = AutoCESForecaster(season_length=12)
>>> forecaster
AutoCESForecaster(...)

Source Code

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class AutoCESForecaster(BaseStatsForecaster):
    """AutoCES (Complex Exponential Smoothing) forecaster via statsforecast.

    Automatically selects the best CES model.

    Parameters
    ----------
    season_length : int, default=1
        Length of the seasonal period.
    model : str, default="Z"
        CES model specification.
    freq : str or None, default=None
        Frequency string. Auto-inferred from data if None.
    feature_transformer : BaseTransformer or None, default=None
        Transformer applied to exogenous features before fitting/predicting.
    target_transformer : BaseTransformer or None, default=None
        Transformer applied to the target before fitting. Inverse-transformed
        after predicting to return forecasts in the original scale.
    target_as_feature : {"transformed", "raw"} or None, default=None
        Whether to include target values as additional features.
    **params : dict
        Additional parameters forwarded to ``statsforecast.models.AutoCES``.

    Attributes
    ----------
    nixtla_forecaster_ : StatsForecast
        The fitted Nixtla orchestrator.
    instance_ : AutoCES
        The constructed AutoCES model instance.

    See Also
    --------
    AutoETSForecaster : Automatic ETS model selection.

    Examples
    --------
    >>> from yohou_nixtla.stats import AutoCESForecaster
    >>> forecaster = AutoCESForecaster(season_length=12)
    >>> forecaster  # doctest: +ELLIPSIS
    AutoCESForecaster(...)

    """

    _estimator_default_class = AutoCES