def forward(
        self,
        y: np.ndarray,
        h: int,
        X: np.ndarray,
        X_future: np.ndarray,
        level: Optional[List[int]] = None,
        fitted: bool = False,
    ):
        r"""Apply fitted SklearnModel to a new/updated time series.

        Parameters
        ----------
        y : numpy.array
            Clean time series of shape (t, ).
        h : int
            Forecast horizon.
        X : array-like
            Insample exogenous of shape (t, n_x).
        X_future : array-like
            Exogenous of shape (h, n_x).
        level : List[float]
            Confidence levels for prediction intervals.
        fitted : bool
            Whether or not returns insample predictions.

        Returns
        -------
        forecasts : dict
            Dictionary with entries `constant` for point predictions and `level_*` for probabilistic predictions.
        """
        if not hasattr(self, "model_"):
            raise Exception("You have to use the `fit` method first")
        res = {"mean": self.model_["model"].predict(X_future)}
        if fitted:
            res["fitted"] = self.model_["model"].predict(X)
        if level is not None:
            level = sorted(level)
            if self.prediction_intervals is not None:
                res = self._add_conformal_intervals(fcst=res, y=y, X=X, level=level)
            else:
                raise Exception("You must pass `prediction_intervals` to compute them.")
            if fitted:
                se = _calculate_sigma(y - res["fitted"], y.size)
                res = _add_fitted_pi(res=res, se=se, level=level)
        return res
Apply fitted SklearnModel to a new/updated time series.

        Parameters
        ----------
        y : numpy.array
            Clean time series of shape (t, ).
        h : int
            Forecast horizon.
        X : array-like
            Insample exogenous of shape (t, n_x).
        X_future : array-like
            Exogenous of shape (h, n_x).
        level : List[float]
            Confidence levels for prediction intervals.
        fitted : bool
            Whether or not returns insample predictions.

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