# Buggy code
def fit(self, y, X=None, fh=None):
    """Fit to training data.

    Parameters
    ----------
    y : pd.Series
        Target time series to which to fit the forecaster.
    fh : int, list or np.array, optional (default=None)
        The forecasters horizon with the steps ahead to to predict.
    X : pd.DataFrame, optional (default=None)
        Exogenous variables are ignored
    Returns
    -------
    self : returns an instance of self.
    """
    # input checks
    if X is not None:
        raise NotImplementedError()

    # set values
    self._set_y_X(y, X)
    self._set_fh(fh)

    self.step_length_ = check_step_length(self.step_length)
    self.window_length_ = check_window_length(self.window_length)

    # set up cv iterator, for recursive strategy, a single estimator
    # is fit for a one-step-ahead forecasting horizon and then called
    # iteratively to predict multiple steps ahead
    self._cv = SlidingWindowSplitter(
        fh=1,
        window_length=self.window_length_,
        step_length=self.step_length_,
        start_with_window=True,
    )

    # transform data into tabular form
    X_train_tab, y_train_tab = self._transform(y, X)

    # fit base regressor
    regressor = clone(self.regressor)
    regressor.fit(X_train_tab, y_train_tab.ravel())
    self.regressor_ = regressor

    self._is_fitted = True
    return self


# Traceback (ValueError)
ValueError                                Traceback (most recent call last)
<ipython-input-3-4a75cca05732> in <module>
16 MODEL_Auto_ARIMA.fit(
17     y_train=y_df["y"],
---> 18     X_train=X_df
19 )

~/miniconda3/envs/RATP37/lib/python3.7/site-packages/sktime/forecasting/arima.py in fit(self, y_train, fh, X_train, **fit_args)
363         self._set_y_X(y_train, X_train)
364         self._set_fh(fh)
--> 365         self._forecaster.fit(y_train, exogenous=X_train, **fit_args)
366         self._is_fitted = True
367         return self

~/miniconda3/envs/RATP37/lib/python3.7/site-packages/pmdarima/arima/auto.py in fit(self, y, X, **fit_args)
218             with_intercept=self.with_intercept,
219             sarimax_kwargs=sarimax_kwargs,
--> 220             **fit_args)
221
222         return self

~/miniconda3/envs/RATP37/lib/python3.7/site-packages/pmdarima/arima/auto.py in auto_arima(y, X, start_p, d, start_q, max_p, max_d, max_q, start_P, D, start_Q, max_P, max_D, max_Q, max_order, m, seasonal, stationary, information_criterion, alpha, test, seasonal_test, stepwise, n_jobs, start_params, trend, method, maxiter, offset_test_args, seasonal_test_args, suppress_warnings, error_action, trace, random, random_state, n_fits, return_valid_fits, out_of_sample_size, scoring, scoring_args, with_intercept, sarimax_kwargs, **fit_args)
390
391     # Temporary shim until we remove `exogenous` support completely
--> 392     X, fit_args = pm_compat.get_X(X, **fit_args)
393
394     # pop out the deprecated kwargs

~/miniconda3/envs/RATP37/lib/python3.7/site-packages/pmdarima/compat/pmdarima.py in get_X(X, **kwargs)
12     exog = kwargs.pop("exogenous", None)
13     if X is not None and exog is not None:
---> 14         raise ValueError("Multiple values provided for both X and exogenous")
15
16     if exog is not None:

ValueError: Multiple values provided for both X and exogenous

# Fixed code
def fit(self, y, X=None, fh=None):
    """Fit to training data.

    Parameters
    ----------
    y : pd.Series
        Target time series to which to fit the forecaster.
    fh : int, list or np.array, optional (default=None)
        The forecasters horizon with the steps ahead to to predict.
    X : pd.DataFrame, optional (default=None)
        Exogenous variables are ignored
    Returns
    -------
    self : returns an instance of self.
    """
    # input checks
    if X is not None:
        raise NotImplementedError(
            "Support for exogenous variables is not yet implemented"
        )

    # set values
    self._set_y_X(y, X)
    self._set_fh(fh)

    self.step_length_ = check_step_length(self.step_length)
    self.window_length_ = check_window_length(self.window_length)

    # set up cv iterator, for recursive strategy, a single estimator
    # is fit for a one-step-ahead forecasting horizon and then called
    # iteratively to predict multiple steps ahead
    self._cv = SlidingWindowSplitter(
        fh=1,
        window_length=self.window_length_,
        step_length=self.step_length_,
        start_with_window=True,
    )

    # transform data into tabular form
    X_train_tab, y_train_tab = self._transform(y, X)

    # fit base regressor
    regressor = clone(self.regressor)
    regressor.fit(X_train_tab, y_train_tab.ravel())
    self.regressor_ = regressor

    self._is_fitted = True
    return self