def METHOD_NAME(
    self,
    n: int,
    future_covariates: Optional[TimeSeries] = None,
    num_samples: int = 1,
    verbose: bool = False,
):
    super().METHOD_NAME(n, future_covariates, num_samples)
    forecast_dict = self.model.predict(
        h=n,
        X=future_covariates.values(copy=False) if future_covariates else None,
        level=(one_sigma_rule,),  # ask one std for the confidence interval.
    )
    mu, std = unpack_sf_dict(forecast_dict)
    if num_samples > 1:
        samples = create_normal_samples(mu, std, num_samples, n)
    else:
        samples = mu
    return self._build_forecast_series(samples)