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

        Uses the average of the last $k$ observations, with $k$ the length of the window.
        Wider windows will capture global trends, while narrow windows will reveal local trends.
        The length of the window selected should take into account the importance of past
        observations and how fast the series changes.

        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.
        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.window_size = window_size
        self.alias = alias
        self.prediction_intervals = prediction_intervals
        self.only_conformal_intervals = True
WindowAverage model.

        Uses the average of the last $k$ observations, with $k$ the length of the window.
        Wider windows will capture global trends, while narrow windows will reveal local trends.
        The length of the window selected should take into account the importance of past
        observations and how fast the series changes.

        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.
        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