models.py 文件源码

def _predict(self, steps, exog, alpha):
        assert 0 < alpha < 1
        y = (exog if exog is not None else self._endog)[-self.results.k_ar:]
        forecast = self.results.forecast(y, steps)
        #  FIXME: The following is adapted from statsmodels's
        # VAR.forecast_interval() as the original doesn't work
        q = norm.ppf(1 - alpha / 2)
        sigma = np.sqrt(np.abs(np.diagonal(self.results.mse(steps), axis1=2)))
        err = q * sigma
        return np.asarray([forecast, forecast - err, forecast + err])