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

        Parameters
        ----------
        h : int
            Forecast horizon.
        X : array-like
            Optional exogenous 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_arima(self.model_, h=h, xreg=X, level=level)
        mean = fcst["mean"]
        res = {"mean": 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 = {
                "mean": mean,
                **{f"lo-{l}": fcst["lower"][f"{l}%"] for l in reversed(level)},
                **{f"hi-{l}": fcst["upper"][f"{l}%"] for l in level},
            }
        return res
Predict with fitted AutoArima.

        Parameters
        ----------
        h : int
            Forecast horizon.
        X : array-like
            Optional exogenous 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