def predict(
        self,
        h: int,
        X: Optional[np.ndarray] = None,
        level: Optional[List[int]] = None,
    ) -> Dict[str, Any]:
        r"""Predict with fitted AutoMFLES.

        Parameters
        ----------
        h : int
            Forecast horizon.
        X : array-like, optional (default=None)
            Exogenous of shape (h, n_x).
        level: List[int]
            Confidence levels (0-100) for prediction intervals.

        Returns
        -------
        forecasts : dict
            Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.
        r"""
        res = {"mean": self.model_["model"].predict(forecast_horizon=h, X=X)}
        if level is None:
            return res
        level = sorted(level)
        if self.prediction_intervals is not None:
            res = self._add_predict_conformal_intervals(res, level)
        else:
            raise Exception("You must pass `prediction_intervals` to compute them.")
        return res
Predict with fitted AutoMFLES.

        Parameters
        ----------
        h : int
            Forecast horizon.
        X : array-like, optional (default=None)
            Exogenous of shape (h, n_x).
        level: List[int]
            Confidence levels (0-100) for prediction intervals.

        Returns
        -------
        forecasts : dict
            Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.
        r
python/statsforecast/models.py