@property
    def standardized_forecasts_error(self):
        if self._standardized_forecasts_error is None:
            from scipy import linalg
            self._standardized_forecasts_error = np.zeros(
                self.forecasts_error.shape, dtype=self.dtype)

            for t in range(self.forecasts_error_cov.shape[2]):
                if self.nmissing[t] > 0:
                    self._standardized_forecasts_error[:, t] = np.nan
                if self.nmissing[t] < self.k_endog:
                    mask = ~self.missing[:, t].astype(bool)
                    F = self.forecasts_error_cov[np.ix_(mask, mask, [t])]
                    upper, _ = linalg.cho_factor(F[:, :, 0])
                    self._standardized_forecasts_error[mask, t] = (
                        linalg.solve_triangular(
                            upper, self.forecasts_error[mask, t]
                        )
                    )

        return self._standardized_forecasts_error
Used by the Jinja templates
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/tsa/statespace/kalman_filter.py/FilterResults.standardized_forecasts_error--crossover with--dataset/ETHPy150Open Netflix/security_monkey/security_monkey/watcher.py/Watcher.created