class_docstr: SARIMAX forecaster.
  
  Direct interface for `statsmodels.tsa.api.SARIMAX`.
  
  Parameters
  ----------
  order : iterable or iterable of iterables, optional, default=(1,0,0)
      The (p,d,q) order of the model for the number of AR parameters,
      differences, and MA parameters. `d` must be an integer
      indicating the integration order of the process, while
      `p` and `q` may either be an integers indicating the AR and MA
      orders (so that all lags up to those orders are included) or else
      iterables giving specific AR and / or MA lags to include. Default is
      an AR(1) model: (1,0,0).
  seasonal_order : iterable, optional, default=(0,0,0,0)
      The (P,D,Q,s) order of the seasonal component of the model for the
      AR parameters, differences, MA parameters, and periodicity.
      `D` must be an integer indicating the integration order of the process,
      while `P` and `Q` may either be an integers indicating the AR and MA
      orders (so that all lags up to those orders are included) or else
      iterables giving specific AR and / or MA lags to include. `s` is an
      integer giving the periodicity (number of periods in season), often it
      is 4 for quarterly data or 12 for monthly data. Default is no seasonal
      effect.
  trend : str{'n','c','t','ct'} or iterable, optional, default="c"
      Parameter controlling the deterministic trend polynomial :math:`A(t)`.
      Can be specified as a string where 'c' indicates a constant (i.e. a
      degree zero component of the trend polynomial), 't' indicates a
      linear trend with time, and 'ct' is both. Can also be specified as an
      iterable defining the non-zero polynomial exponents to include, in
      increasing order. For example, `[1,1,0,1]` denotes
      :math:`a + bt + ct^3`. Default is to not include a trend component.
  measurement_error : bool, optional, default=False
      Whether or not to assume the endogenous observations `endog` were
      measured with error.
  time_varying_regression : bool, optional, default=False
      Used when an explanatory variables, `exog`, are provided
      to select whether or not coefficients on the exogenous regressors are
      allowed to vary over time.
  mle_regression : bool, optional, default=True
      Whether or not to use estimate the regression coefficients for the
      exogenous variables as part of maximum likelihood estimation or through
      the Kalman filter (i.e. recursive least squares). If
      `time_varying_regression` is True, this must be set to False.
  simple_differencing : bool, optional, default=False
      Whether or not to use partially conditional maximum likelihood
      estimation. If True, differencing is performed prior to estimation,
      which discards the first :math:`s D + d` initial rows but results in a
      smaller state-space formulation. See the Notes section for important
      details about interpreting results when this option is used. If False,
      the full SARIMAX model is put in state-space form so that all
      datapoints can be used in estimation.
  enforce_stationarity : bool, optional, default=True
      Whether or not to transform the AR parameters to enforce stationarity
      in the autoregressive component of the model.
  enforce_invertibility : bool, optional, default=True
      Whether or not to transform the MA parameters to enforce invertibility
      in the moving average component of the model.
  hamilton_representation : bool, optional, default=False
      Whether or not to use the Hamilton representation of an ARMA process
      (if True) or the Harvey representation (if False).
  concentrate_scale : bool, optional, default=False
      Whether or not to concentrate the scale (variance of the error term)
      out of the likelihood. This reduces the number of parameters estimated
      by maximum likelihood by one, but standard errors will then not
      be available for the scale parameter.
  trend_offset : int, optional, default=1
      The offset at which to start time trend values. Default is 1, so that
      if `trend='t'` the trend is equal to 1, 2, ..., nobs. Typically is only
      set when the model created by extending a previous dataset.
  use_exact_diffuse : bool, optional, default=False
      Whether or not to use exact diffuse initialization for non-stationary
      states. Default is False (in which case approximate diffuse
      initialization is used).
  random_state : int, RandomState instance or None, optional ,
      default=None – If int, random_state is the seed used by the random
      number generator; If RandomState instance, random_state is the random
      number generator; If None, the random number generator is the
      RandomState instance used by np.random.
  
  See Also
  --------
  ARIMA
  AutoARIMA
  StatsForecastAutoARIMA
  
  References
  ----------
  .. [1] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles
  and practice. OTexts, 2014.
  
  Examples
  --------
  >>> from sktime.datasets import load_airline
  >>> from sktime.forecasting.sarimax import SARIMAX
  >>> y = load_airline()
  >>> forecaster = SARIMAX(
  ...     order=(1, 0, 0), trend="t", seasonal_order=(1, 0, 0, 6))  # doctest: +SKIP
  ... )
  >>> forecaster.fit(y)  # doctest: +SKIP
  SARIMAX(...)
  >>> y_pred = forecaster.predict(fh=y.index)  # doctest: +SKIP
docstr: Construct confidence interval at specified `alpha` for each timestep.
  
  Parameters
  ----------
  prediction_results : PredictionResults
      results class, as returned by ``self._fitted_forecaster.get_prediction``
  alpha : float
      one minus nominal coverage
  
  Returns
  -------
  pd.DataFrame
      confidence intervals at each timestep
  
      The dataframe must have at least two columns ``lower`` and ``upper``, and
      the row indices must be integers relative to ``self.cutoff``. Order of
      columns do not matter, and row indices must be a superset of relative
      integer horizon of ``fh``.
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code: def _extract_conf_int(prediction_results, alpha) -> pd.DataFrame:
      """Construct confidence interval at specified `alpha` for each timestep.
  
      Parameters
      ----------
      prediction_results : PredictionResults
          results class, as returned by ``self._fitted_forecaster.get_prediction``
      alpha : float
          one minus nominal coverage
  
      Returns
      -------
      pd.DataFrame
          confidence intervals at each timestep
  
          The dataframe must have at least two columns ``lower`` and ``upper``, and
          the row indices must be integers relative to ``self.cutoff``. Order of
          columns do not matter, and row indices must be a superset of relative
          integer horizon of ``fh``.
      """
      conf_int = prediction_results.conf_int(alpha=alpha)
      conf_int.columns = ["lower", "upper"]
  
      return conf_int
  
file_path: sktime/sktime/forecasting/sarimax.py
imports: import pandas
  from sktime.forecasting.base.adapters import _StatsModelsAdapter
label_desc: Use image node_id 4 for calling the SARIMAX obj's underlying member method code with example usage: obj._extract_conf_int(prediction_results, alpha) and returns: conf_int