def __init__(
        self,
        season_length: int,
        window_size: int,
        alias: str = "SeasWA",
        prediction_intervals: Optional[ConformalIntervals] = None,
    ):
        r"""SeasonalWindowAverage model.

        An average of the last $k$ observations of the same period, with $k$ the length of the window.

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

        Parameters
        ----------
        window_size : int
            Size of truncated series on which average is estimated.
        seasonal_length : int
            Number of observations per cycle.
        alias : str
            Custom name of the model.
        prediction_intervals : Optional[ConformalIntervals]
            Information to compute conformal prediction intervals.
            This is required for generating future prediction intervals.
        r"""
        self.season_length = season_length
        self.window_size = window_size
        self.alias = alias
        self.prediction_intervals = prediction_intervals
        self.only_conformal_intervals = True
SeasonalWindowAverage model.

        An average of the last $k$ observations of the same period, with $k$ the length of the window.

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

        Parameters
        ----------
        window_size : int
            Size of truncated series on which average is estimated.
        seasonal_length : int
            Number of observations per cycle.
        alias : str
            Custom name of the model.
        prediction_intervals : Optional[ConformalIntervals]
            Information to compute conformal prediction intervals.
            This is required for generating future prediction intervals.
        r
python/statsforecast/models.py