def fit(
        self,
        y: np.ndarray,
        X: np.ndarray,
    ) -> "SklearnModel":
        r"""Fit the model.

        Parameters
        ----------
        y : numpy.array
            Clean time series of shape (t, ).
        X : array-like
            Exogenous of shape (t, n_x).

        Returns
        -------
        self : SklearnModel
            Fitted SklearnModel object.
        r"""
        from sklearn.base import clone

        self.model_ = {"model": clone(self.model)}
        self.model_["model"].fit(X, y)
        self._store_cs(y=y, X=X)
        self.model_["fitted"] = self.model_["model"].predict(X)
        residuals = y - self.model_["fitted"]
        self.model_["sigma"] = _calculate_sigma(residuals, y.size)
        return self
Fit the model.

        Parameters
        ----------
        y : numpy.array
            Clean time series of shape (t, ).
        X : array-like
            Exogenous of shape (t, n_x).

        Returns
        -------
        self : SklearnModel
            Fitted SklearnModel object.
        r
python/statsforecast/models.py