class_docstr: Wrapper of the pmdarima implementation of fitting (S)ARIMA(X) models.
  
  Exposes `pmdarima.arima.ARIMA` [1]_ under the `sktime` interface.
  Seasonal ARIMA models and exogeneous input is supported, hence this estimator is
  capable of fitting SARIMA, ARIMAX, and SARIMAX.
  To additionally fit (S)ARIMA(X) hyper-parameters, use the `AutoARIMA` estimator.
  
  An ARIMA, or autoregressive integrated moving average model, is a
  generalization of an autoregressive moving average (ARMA) model, and is fitted to
  time-series data in an effort to forecast future points. ARIMA models can
  be especially efficacious in cases where data shows evidence of
  non-stationarity.
  
  The "AR" part of ARIMA indicates that the evolving variable of interest is
  regressed on its own lagged (i.e., prior observed) values. The "MA" part
  indicates that the regression error is actually a linear combination of
  error terms whose values occurred contemporaneously and at various times
  in the past. The "I" (for "integrated") indicates that the data values
  have been replaced with the difference between their values and the
  previous values (and this differencing process may have been performed
  more than once). The purpose of each of these features is to make the model
  fit the data as well as possible.
  
  Non-seasonal ARIMA models are generally denoted ``ARIMA(p,d,q)`` where
  parameters ``p``, ``d``, and ``q`` are non-negative integers, ``p`` is the
  order (number of time lags) of the autoregressive model, ``d`` is the
  degree of differencing (the number of times the data have had past values
  subtracted), and ``q`` is the order of the moving-average model. Seasonal
  ARIMA models are usually denoted ``ARIMA(p,d,q)(P,D,Q)m``, where ``m``
  refers to the number of periods in each season, and the uppercase ``P``,
  ``D``, ``Q`` refer to the autoregressive, differencing, and moving average
  terms for the seasonal part of the ARIMA model.
  
  When two out of the three terms are zeros, the model may be referred to
  based on the non-zero parameter, dropping "AR", "I" or "MA" from the
  acronym describing the model. For example, ``ARIMA(1,0,0)`` is ``AR(1)``,
  ``ARIMA(0,1,0)`` is ``I(1)``, and ``ARIMA(0,0,1)`` is ``MA(1)``.
  
  See notes for more practical information on the ``ARIMA`` class.
  
  Parameters
  ----------
  order : iterable or array-like, shape=(3,), optional (default=(1, 0, 0))
      The (p,d,q) order of the model for the number of AR parameters,
      differences, and MA parameters to use. ``p`` is the order (number of
      time lags) of the auto-regressive model, and is a non-negative integer.
      ``d`` is the degree of differencing (the number of times the data have
      had past values subtracted), and is a non-negative integer. ``q`` is
      the order of the moving-average model, and is a non-negative integer.
      Default is an AR(1) model: (1,0,0).
  seasonal_order : array-like, shape=(4,), 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.
  start_params : array-like, optional (default=None)
      Starting parameters for ``ARMA(p,q)``.  If None, the default is given
      by ``ARMA._fit_start_params``.
  method : str, optional (default='lbfgs')
      The ``method`` determines which solver from ``scipy.optimize``
      is used, and it can be chosen from among the following strings:
  
      - 'newton' for Newton-Raphson
      - 'nm' for Nelder-Mead
      - 'bfgs' for Broyden-Fletcher-Goldfarb-Shanno (BFGS)
      - 'lbfgs' for limited-memory BFGS with optional box constraints
      - 'powell' for modified Powell's method
      - 'cg' for conjugate gradient
      - 'ncg' for Newton-conjugate gradient
      - 'basinhopping' for global basin-hopping solver
  
      The explicit arguments in ``fit`` are passed to the solver,
      with the exception of the basin-hopping solver. Each
      solver has several optional arguments that are not the same across
      solvers. These can be passed as **fit_kwargs
  maxiter : int, optional (default=50)
      The maximum number of function evaluations. Default is 50
  suppress_warnings : bool, optional (default=False)
      Many warnings might be thrown inside of statsmodels. If
      ``suppress_warnings`` is True, all of these warnings will be squelched.
  out_of_sample_size : int, optional (default=0)
      The number of examples from the tail of the time series to hold out
      and use as validation examples. The model will not be fit on these
      samples, but the observations will be added into the model's ``endog``
      and ``exog`` arrays so that future forecast values originate from the
      end of the endogenous vector. See :func:`update`.
      For instance::
          y = [0, 1, 2, 3, 4, 5, 6]
          out_of_sample_size = 2
          > Fit on: [0, 1, 2, 3, 4]
          > Score on: [5, 6]
          > Append [5, 6] to end of self.arima_res_.data.endog values
  scoring : str or callable, optional (default='mse')
      If performing validation (i.e., if ``out_of_sample_size`` > 0), the
      metric to use for scoring the out-of-sample data:
  
      - If a string, must be a valid metric name importable from
        ``sklearn.metrics``
  
      - If a callable, must adhere to the function signature::
  
          def foo_loss(y_true, y_pred)
  
      Note that models are selected by *minimizing* loss. If using a
      maximizing metric (such as ``sklearn.metrics.r2_score``), it is the
      user's responsibility to wrap the function such that it returns a
      negative value for minimizing.
  scoring_args : dict, optional (default=None)
      A dictionary of key-word arguments to be passed to the
      ``scoring`` metric.
  trend : str or None, optional (default=None)
      The trend parameter. If ``with_intercept`` is True, ``trend`` will be
      used. If ``with_intercept`` is False, the trend will be set to a no-
      intercept value. If None and ``with_intercept``, 'c' will be used as
      a default.
  with_intercept : bool, optional (default=True)
      Whether to include an intercept term. Default is True.
  Further arguments to pass to the SARIMAX constructor:
  - time_varying_regression : boolean, optional (default=False)
      Whether or not coefficients on the exogenous regressors are allowed
      to vary over time.
  - enforce_stationarity : boolean, optional (default=True)
      Whether or not to transform the AR parameters to enforce
      stationarity in the auto-regressive component of the model.
      - enforce_invertibility : boolean, optional (default=True)
      Whether or not to transform the MA parameters to enforce
      invertibility in the moving average component of the model.
  - simple_differencing : boolean, optional (default=False)
      Whether or not to use partially conditional maximum likelihood
      estimation for seasonal ARIMA models. 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. If False, the full SARIMAX model is
      put in state-space form so that all datapoints can be used in
      estimation. Default is False.
  - measurement_error: boolean, optional (default=False)
      Whether or not to assume the endogenous observations endog were
      measured with error. Default is False.
  - mle_regression : boolean, 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.
      Default is True.
  - hamilton_representation : boolean, optional (default=False)
      Whether or not to use the Hamilton representation of an ARMA
      process (if True) or the Harvey representation (if False).
      Default is False.
  - concentrate_scale : boolean, 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.
  
  See Also
  --------
  AutoARIMA
  
  References
  ----------
  .. [1] https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.ARIMA.html
  
  .. [2] https://www.statsmodels.org/stable/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html
  
  Examples
  --------
  >>> from sktime.datasets import load_airline
  >>> from sktime.forecasting.arima import ARIMA
  >>> y = load_airline()
  >>> forecaster = ARIMA(  # doctest: +SKIP
  ...     order=(1, 1, 0),
  ...     seasonal_order=(0, 1, 0, 12),
  ...     suppress_warnings=True)
  >>> forecaster.fit(y)  # doctest: +SKIP
  ARIMA(...)
  >>> y_pred = forecaster.predict(fh=[1,2,3])  # 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.
  
  Returns
  -------
  params : dict or list of dict
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.
  
      Returns
      -------
      params : dict or list of dict
      """
      params1 = {"maxiter": 3}
      params2 = {
          "order": (1, 1, 0),
          "seasonal_order": (1, 0, 0, 2),
          "maxiter": 3,
      }
      return [params1, params2]
  
file_path: sktime/sktime/forecasting/arima.py
imports: from sktime.forecasting.base.adapters._pmdarima import _PmdArimaAdapter
label_desc: Use image node_id 3 for calling the ARIMA obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list