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

        All forecasts have the value of the last observation:
        $\hat{y}_{t+1} = y_t$ for all $t$

        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
Naive model.

        All forecasts have the value of the last observation:
        $\hat{y}_{t+1} = y_t$ for all $t$

        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