class_docstr: Autoregressive AR-X(p) model.
  
  Estimate an AR-X model using Conditional Maximum Likelihood (OLS).
  
  Parameters
  ----------
  endog : array_like
      A 1-d endogenous response variable. The dependent variable.
  lags : {None, int, list[int]}
      The number of lags to include in the model if an integer or the
      list of lag indices to include.  For example, [1, 4] will only
      include lags 1 and 4 while lags=4 will include lags 1, 2, 3, and 4.
      None excludes all AR lags, and behave identically to 0.
  trend : {'n', 'c', 't', 'ct'}
      The trend to include in the model:
  
      * 'n' - No trend.
      * 'c' - Constant only.
      * 't' - Time trend only.
      * 'ct' - Constant and time trend.
  
  seasonal : bool
      Flag indicating whether to include seasonal dummies in the model. If
      seasonal is True and trend includes 'c', then the first period
      is excluded from the seasonal terms.
  exog : array_like, optional
      Exogenous variables to include in the model. Must have the same number
      of observations as endog and should be aligned so that endog[i] is
      regressed on exog[i].
  hold_back : {None, int}
      Initial observations to exclude from the estimation sample.  If None,
      then hold_back is equal to the maximum lag in the model.  Set to a
      non-zero value to produce comparable models with different lag
      length.  For example, to compare the fit of a model with lags=3 and
      lags=1, set hold_back=3 which ensures that both models are estimated
      using observations 3,...,nobs. hold_back must be >= the maximum lag in
      the model.
  period : {None, int}
      The period of the data. Only used if seasonal is True. This parameter
      can be omitted if using a pandas object for endog that contains a
      recognized frequency.
  missing : str
      Available options are 'none', 'drop', and 'raise'. If 'none', no nan
      checking is done. If 'drop', any observations with nans are dropped.
      If 'raise', an error is raised. Default is 'none'.
  deterministic : DeterministicProcess
      A deterministic process.  If provided, trend and seasonal are ignored.
      A warning is raised if trend is not "n" and seasonal is not False.
  
  Examples
  --------
  Use AutoREG to forecast univariate data.
  
  >>> from sktime.forecasting.auto_reg import AutoREG  # doctest: +SKIP
  >>> from sktime.datasets import load_airline
  >>> from sktime.forecasting.base import ForecastingHorizon
  >>> data = load_airline()
  >>> autoreg_sktime = AutoREG(lags=2, trend="c")  # doctest: +SKIP
  >>> autoreg_sktime.fit(y=data)  # doctest: +SKIP
  AutoREG(lags=2)
  >>> fh = ForecastingHorizon([x for x in range(1, 13)])
  >>> y_pred = autoreg_sktime.predict(fh=fh)  # doctest: +SKIP
  
  
  Use AutoREG to forecast with exogenous data.
  
  >>> from sktime.forecasting.auto_reg import AutoREG  # doctest: +SKIP
  >>> from sktime.datasets import load_longley
  >>> from sktime.forecasting.base import ForecastingHorizon
  >>> y, X_og = load_longley()
  >>> X_oos = X_og.iloc[-5:, :]
  >>> y, X = y.iloc[:-5], X_og.iloc[:-5, :]
  >>> X, X_oos = X[["GNPDEFL", "GNP"]], X_oos[["GNPDEFL", "GNP"]]
  >>> autoreg_sktime = AutoREG(lags=2, trend="c")  # doctest: +SKIP
  >>> autoreg_sktime.fit(y=y, X=X)  # doctest: +SKIP
  AutoREG(lags=2)
  >>> fh = ForecastingHorizon([x for x in range(1, 4)])
  >>> y_pred = autoreg_sktime.predict(X=X_oos, fh=fh)  # doctest: +SKIP
docstr: Return testing parameter settings for the estimator.
  
  Parameters
  ----------
  parameter_set : str, default="default"
      Name of the set of test parameters to return, for use in tests. If no
      special parameters are defined for a value, will return `"default"` set.
      There are currently no reserved values for forecasters.
  
  Returns
  -------
  params : dict or list of dict, default = {}
      Parameters to create testing instances of the class
      Each dict are parameters to construct an "interesting" test instance, i.e.,
      `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
      `create_test_instance` uses the first (or only) dictionary in `params`
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code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the estimator.
  
      Parameters
      ----------
      parameter_set : str, default="default"
          Name of the set of test parameters to return, for use in tests. If no
          special parameters are defined for a value, will return `"default"` set.
          There are currently no reserved values for forecasters.
  
      Returns
      -------
      params : dict or list of dict, default = {}
          Parameters to create testing instances of the class
          Each dict are parameters to construct an "interesting" test instance, i.e.,
          `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
          `create_test_instance` uses the first (or only) dictionary in `params`
      """
      params = [
          {"lags": 2, "trend": "c"},
          {"lags": 2, "trend": "ct"},
          {"lags": 2, "trend": "t", "seasonal": True, "period": 2},
      ]
      return params
  
imports: from sktime.forecasting.base.adapters import _StatsModelsAdapter
label_desc: Use image node_id 4 for calling the AutoREG obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: params