def forecast(self, steps=1, exog=None, alpha=.05):
        """
        Out-of-sample forecasts

        Parameters
        ----------
        steps : int
            The number of out of sample forecasts from the end of the
            sample.
        exog : array
            If the model is an ARIMAX, you must provide out of sample
            values for the exogenous variables. This should not include
            the constant.
        alpha : float
            The confidence intervals for the forecasts are (1 - alpha) %

        Returns
        -------
        forecast : array
            Array of out of sample forecasts
        stderr : array
            Array of the standard error of the forecasts.
        conf_int : array
            2d array of the confidence interval for the forecast

        Notes
        -----
        Prediction is done in the levels of the original endogenous variable.
        If you would like prediction of differences in levels use `predict`.
        """
        if exog is not None:
            if self.k_exog >= 1 and exog.ndim == 1:
                exog = exog[:, None]
            if exog.shape[0] != steps:
                raise ValueError("new exog needed for each step")
            # prepend in-sample exog observations
            if self.k_ar > 0:
                exog = np.vstack((self.model.exog[-self.k_ar:, self.k_trend:],
                                  exog))
        forecast = _arma_predict_out_of_sample(self.params, steps, self.resid,
                                               self.k_ar, self.k_ma,
                                               self.k_trend, self.k_exog,
                                               self.model.endog,
                                               exog, method=self.model.method)

        d = self.k_diff
        endog = self.model.data.endog[-d:]
        forecast = unintegrate(forecast, unintegrate_levels(endog, d))[d:]

        # get forecast errors
        fcerr = self._forecast_error(steps)
        conf_int = self._forecast_conf_int(forecast, fcerr, alpha)
        return forecast, fcerr, conf_int
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/tsa/arima_model.py/ARIMAResults.forecast/operator `==` -> `>=`