def _compute_pred_errors(self, alpha=DEFAULT_ALPHA):
"""
Get the prediction errors for the forecast.
"""
self.check_is_fitted()
alpha = check_alpha(alpha)
n_timepoints = len(self.oh)
self.sigma_ = np.sqrt(self._fitted_forecaster.sse / (n_timepoints - 1))
sem = self.sigma_ * np.sqrt(self._fh * self.smoothing_level_ ** 2 + 1)
errors = []
for a in alpha:
z = zscore(1 - a)
error = z * sem
errors.append(
pd.Series(error, index=self.fh.absolute(self.cutoff)))
# for a single alpha value, unwrap list
if len(errors) == 1:
return errors[0]
# otherwise, return list of errors
return errors
def _compute_pred_err(self, alphas):
"""
Get the prediction errors for the forecast.
"""
self.check_is_fitted()
n_timepoints = len(self._y)
self.sigma_ = np.sqrt(self._fitted_forecaster.sse / (n_timepoints - 1))
sem = self.sigma_ * np.sqrt(self._fh * self.smoothing_level_ ** 2 + 1)
errors = []
for alpha in alphas:
z = zscore(1 - alpha)
error = z * sem
errors.append(
pd.Series(error, index=self.fh.absolute(self.cutoff)))
return errors