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

        Also known as mean method. Uses a simple average of all past observations.
        Assuming there are $t$ observations, the one-step forecast is given by:
        $$\hat{y}_{t+1} = \frac{1}{t} \sum_{j=1}^t y_j$$

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

        Parameters
        ----------
        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.alias = alias
        self.prediction_intervals = prediction_intervals
HistoricAverage model.

        Also known as mean method. Uses a simple average of all past observations.
        Assuming there are $t$ observations, the one-step forecast is given by:
        $$\hat{y}_{t+1} = \frac{1}{t} \sum_{j=1}^t y_j$$

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

        Parameters
        ----------
        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