

def _get_predict_end(self, end, dynamic=False):
    '\n        Returns last index to be forecast of the differenced array.\n        Handling of inclusiveness should be done in the predict function.\n        '
    (end, out_of_sample) = super(ARIMA, self)._get_predict_end(end, dynamic)
    if (('mle' not in self.method) and (not dynamic)):
        end -= self.k_ar
    return ((end - self.k_diff), out_of_sample)

dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/tsa/arima_model.py ARIMA._get_predict_end/original