# Buggy code
def _transform(self, y_train, X_train=None):
    """Transform data using rolling window approach"""
    if X_train is not None:
        raise NotImplementedError()
    y_train = check_y(y_train)

    # get integer time index
    cv = self._cv

    # Transform target series into tabular format using
    # rolling window tabularisation
    x_windows = []
    y_windows = []
    for x_index, y_index in cv.split(y_train):
        x_window = y_train.iloc[x_index]
        y_window = y_train.iloc[y_index]

        x_windows.append(x_window)
        y_windows.append(y_window)

    # Put into required input format for regression
    X_train, y_train = self._format_windows(x_windows, y_windows)
    return X_train, y_train


# 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 _transform(self, y, X=None):
    """Transform data using rolling window approach"""
    if X is not None:
        raise NotImplementedError()
    y = check_y(y)

    # get integer time index
    cv = self._cv

    # Transform target series into tabular format using
    # rolling window tabularisation
    x_windows = []
    y_windows = []
    for x_index, y_index in cv.split(y):
        x_window = y.iloc[x_index]
        y_window = y.iloc[y_index]

        x_windows.append(x_window)
        y_windows.append(y_window)

    # Put into required input format for regression
    X, y = self._format_windows(x_windows, y_windows)
    return X, y