def predict(
        self, h: int, X: Optional[np.ndarray] = None, level: Optional[List[int]] = None
    ):
        r"""Predict with fitted Exponential Smoothing.

        Parameters
        ----------
        h : int
            Forecast horizon.
        X : array-like
            Optional exogenpus of shape (h, n_x).
        level : List[float]
            Confidence levels (0-100) for prediction intervals.

        Returns
        -------
        forecasts : dict
            Dictionary with entries `mean` for point predictions and `level_*` for probabilistic predictions.
        """
        fcst = forecast_ets(self.model_, h=h, level=level)
        res = {"mean": fcst["mean"]}
        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:
            res = {
                **res,
                **{f"lo-{l}": fcst[f"lo-{l}"] for l in reversed(level)},
                **{f"hi-{l}": fcst[f"hi-{l}"] for l in level},
            }
        return res
Predict with fitted Exponential Smoothing.

        Parameters
        ----------
        h : int
            Forecast horizon.
        X : array-like
            Optional exogenpus of shape (h, n_x).
        level : List[float]
            Confidence levels (0-100) for prediction intervals.

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