def fit(self, y, exogenous=None):
        """Fit the transformer

        Learns the value of ``lmbda``, if not specified in the constructor.
        If defined in the constructor, is not re-learned.

        Parameters
        ----------
        y : array-like or None, shape=(n_samples,)
            The endogenous (time-series) array.

        exogenous : array-like or None, shape=(n_samples, n_features), optional
            The exogenous array of additional covariates. Not used for
            endogenous transformers. Default is None, and non-None values will
            serve as pass-through arrays.
        """
        lam1 = self.lmbda
        lam2 = self.lmbda2

        if lam2 < 0:
            raise ValueError("lmbda2 must be a non-negative scalar value")

        if lam1 is None:
            y, _ = self._check_y_exog(y, exogenous)
            _, lam1 = stats.boxcox(y, lmbda=None, alpha=None)

        self.lam1_ = lam1
        self.lam2_ = lam2
        return self
Fit the transformer

        Learns the value of ``lmbda``, if not specified in the constructor.
        If defined in the constructor, is not re-learned.

        Parameters
        ----------
        y : array-like or None, shape=(n_samples,)
            The endogenous (time-series) array.

        exogenous : array-like or None, shape=(n_samples, n_features), optional
            The exogenous array of additional covariates. Not used for
            endogenous transformers. Default is None, and non-None values will
            serve as pass-through arrays.