NaiveForecaster

yohou_nixtla.stats.NaiveForecaster

Bases: BaseStatsForecaster

Naive forecaster via statsforecast.

Repeats the last observed value for all forecast steps.

Parameters

Name	Type	Description	Default
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.Naive.	{}

Attributes

Name	Type	Description
nixtla_forecaster_	StatsForecast	The fitted Nixtla orchestrator.
instance_	Naive	The constructed Naive model instance.

See Also

SeasonalNaiveForecaster : Seasonal naive baseline.

Examples

>>> from yohou_nixtla.stats import NaiveForecaster
>>> forecaster = NaiveForecaster()
>>> forecaster
NaiveForecaster(...)

Source Code

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class NaiveForecaster(BaseStatsForecaster):
    """Naive forecaster via statsforecast.

    Repeats the last observed value for all forecast steps.

    Parameters
    ----------
    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.Naive``.

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

    See Also
    --------
    SeasonalNaiveForecaster : Seasonal naive baseline.

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

    """

    _estimator_default_class = Naive

Tutorials

The following example notebooks use this component:

• How to Forecast Panel Data

  ---

  Forecast multiple related time series using the panel column naming convention.

  View · Open in marimo