def __init__(
        self,
        season_length: int,
        alias: str = "SeasonalNaive",
        prediction_intervals: Optional[ConformalIntervals] = None,
    ):
        r"""Seasonal naive model.

        A method similar to the naive, but uses the last known observation of the same period (e.g. the same month of the previous year) in order to capture seasonal variations.

        References
        ----------
        [Rob J. Hyndman and George Athanasopoulos (2018). "forecasting principles and practice, Simple Methods"](https://otexts.com/fpp3/simple-methods.html#seasonal-na%C3%AFve-method).

        Parameters
        ----------
        season_length : int
            Number of observations per unit of time. Ex: 24 Hourly data.
        alias : str
            Custom name of the model.
        prediction_intervals : Optional[ConformalIntervals]
            Information to compute conformal prediction intervals.
            By default, the model will compute the native prediction
            intervals.
        """
        self.season_length = season_length
        self.alias = alias
        self.prediction_intervals = prediction_intervals
Seasonal naive model.

        A method similar to the naive, but uses the last known observation of the same period (e.g. the same month of the previous year) in order to capture seasonal variations.

        References
        ----------
        [Rob J. Hyndman and George Athanasopoulos (2018). "forecasting principles and practice, Simple Methods"](https://otexts.com/fpp3/simple-methods.html#seasonal-na%C3%AFve-method).

        Parameters
        ----------
        season_length : int
            Number of observations per unit of time. Ex: 24 Hourly data.
        alias : str
            Custom name of the model.
        prediction_intervals : Optional[ConformalIntervals]
            Information to compute conformal prediction intervals.
            By default, the model will compute the native prediction
            intervals.
        
python/statsforecast/models.py