class_docstr: StatsForecast AutoARIMA estimator.
  
  Direct interface to ``statsforecast.models.AutoARIMA`` by Nixtla.
  
  This estimator directly interfaces ``AutoARIMA``,
  from ``statsforecast`` [2]_ by Nixtla.
  The ``statsforecast`` implementation is inspired
  by Hyndman's forecast::auto.arima [1]_.
  
  Returns best ARIMA model according to either AIC, AICc or BIC value.
  The function conducts a search over possible model within
  the order constraints provided.
  
  Parameters
  ----------
  start_p: int (default 2)
      Starting value of p in stepwise procedure.
  d: int optional (default None)
      Order of first-differencing.
      If missing, will choose a value based on `test`.
  start_q: int (default 2)
      Starting value of q in stepwise procedure.
  max_p: int (default 5)
      Maximum value of p.
  max_d: int (default 2)
      Maximum number of non-seasonal differences
  max_q: int (default 5)
      Maximum value of q.
  start_P: int (default 1)
      Starting value of P in stepwise procedure.
  D: int optional (default None)
      Order of seasonal-differencing.
      If missing, will choose a value based on `season_test`.
  start_Q: int (default 1)
      Starting value of Q in stepwise procedure.
  max_P: int (default 2)
      Maximum value of P.
  max_D: int (default 1)
      Maximum number of seasonal differences
  max_Q: int (default 2)
      Maximum value of Q.
  max_order: int (default 5)
      Maximum value of p+q+P+Q if model selection is not stepwise.
  sp: int (default 1)
      Number of observations per unit of time.
      For example 24 for Hourly data.
  seasonal: bool (default True)
      If False, restricts search to non-seasonal models.
  stationary: bool (default False)
      If True, restricts search to stationary models.
  information_criterion: str (default 'aicc')
      Information criterion to be used in model selection.
      It can be chosen from among the following strings:
      - 'aicc' for Akaike's information criterion corrected.
      - 'aic' for Akaike's information criterion.
      - 'bic' for bayesian information criterion.
  test: str (default 'kpss')
      Type of unit root test to use. See ndiffs for details.
      Only 'kpss' for the Kwiatkowski-Phillip-Schmidt-Shin test
      is allowed.
  seasonal_test: str (default 'seas')
      This determines which method is used to select the number
      of seasonal differences.
      The default method ('seas') is to use a measure of seasonal
      strength computed from an STL decomposition.
      Other possibilities involve seasonal unit root tests.
      Only 'seas' is allowed.
  stepwise: bool (default True)
      If True, will do stepwise selection (faster).
      Otherwise, it searches over all models.
      Non-stepwise selection can be very slow,
      especially for seasonal models.
  n_jobs: int (default 2)
      Allows the user to specify the amount of parallel processes to be used
      if parallel = True and stepwise = False.
      If None, then the number of logical cores is
      automatically detected and all available cores are used.
  trend: bool (default True)
      If True, models with drift terms are considered.
  method: str optional (default None)
      fitting method: maximum likelihood or minimize conditional
      sum-of-squares.
      The default (unless there are missing values)
      is to use conditional-sum-of-squares to find starting values,
      then maximum likelihood. Can be abbreviated.
      It can be chosen from among the following strings:
  
      - 'CSS-ML' for conditional sum-of-squares to find starting values and
        then maximum likelihood.
      - 'ML' for maximum likelihood.
      - 'CSS' for conditional sum-of-squares.
  
  offset_test_args: dict optional (default None)
      Additional arguments to be passed to the unit root test.
  seasonal_test_args: dict optional (default None)
      Additional arguments to be passed to the seasonal
      unit root test. See nsdiffs for details.
  trace: bool (default False)
      If True, the list of ARIMA models considered will be reported.
  n_fits: int (default 94)
      Maximum number of models considered in the stepwise search.
  with_intercept: bool (default True)
      If True, models with a non-zero mean are considered.
  approximation: bool optional (default None)
      If True, estimation is via conditional sums of squares
      and the information criteria used for model
      selection are approximated.
      The final model is still computed using
      maximum likelihood estimation.
      Approximation should be used for long time series
      or a high seasonal period to avoid excessive computation times.
  truncate: bool optional (default None)
      An integer value indicating how many observations
      to use in model selection.
      The last truncate values of the series are
      used to select a model when truncate is not None
      and approximation=True.
      All observations are used if either truncate=None
      or approximation=False.
  blambda: float optional (default None)
      Box-Cox transformation parameter.
      If lambda="auto", then a transformation is automatically
      selected using BoxCox.lambda.
      The transformation is ignored if None.
      Otherwise, data transformed before model is estimated.
  biasadj: bool (default False)
      Use adjusted back-transformed mean for Box-Cox transformations.
      If transformed data is used to produce forecasts and fitted values,
      a regular back transformation will result in median forecasts.
      If biasadj is True, an adjustment will be made to produce
      mean forecasts and fitted values.
  parallel: bool (default False)
      If True and stepwise = False, then the specification search
      is done in parallel.
      This can give a significant speedup on multicore machines.
  
  References
  ----------
  .. [1] https://github.com/robjhyndman/forecast
  .. [2] https://github.com/Nixtla/statsforecast
  
  Examples
  --------
  >>> from sktime.datasets import load_airline
  >>> from sktime.forecasting.statsforecast import StatsForecastAutoARIMA
  >>> y = load_airline()
  >>> forecaster = StatsForecastAutoARIMA(  # doctest: +SKIP
  ...     sp=12, d=0, max_p=2, max_q=2
  ... )
  >>> forecaster.fit(y)  # doctest: +SKIP
  StatsForecastAutoARIMA(...)
  >>> y_pred = forecaster.predict(fh=[1,2,3])  # doctest: +SKIP
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code: def __init__(
      self,
      start_p: int = 2,
      d: Optional[int] = None,
      start_q: int = 2,
      max_p: int = 5,
      max_d: int = 2,
      max_q: int = 5,
      start_P: int = 1,
      D: Optional[int] = None,
      start_Q: int = 1,
      max_P: int = 2,
      max_D: int = 1,
      max_Q: int = 2,
      max_order: int = 5,
      sp: int = 1,
      seasonal: bool = True,
      stationary: bool = False,
      information_criterion: str = "aicc",
      test: str = "kpss",
      seasonal_test: str = "seas",
      stepwise: bool = True,
      n_jobs: int = 2,
      trend: bool = True,
      method: Optional[str] = None,
      offset_test_args: Optional[str] = None,
      seasonal_test_args: Optional[Dict] = None,
      trace: bool = False,
      n_fits: int = 94,
      with_intercept: bool = True,
      approximation: Optional[bool] = None,
      truncate: Optional[bool] = None,
      blambda: Optional[float] = None,
      biasadj: bool = False,
      parallel: bool = False,
  ):
      self.start_p = start_p
      self.d = d
      self.start_q = start_q
      self.max_p = max_p
      self.max_d = max_d
      self.max_q = max_q
      self.start_P = start_P
      self.D = D
      self.start_Q = start_Q
      self.max_P = max_P
      self.max_D = max_D
      self.max_Q = max_Q
      self.max_order = max_order
      self.sp = sp
      self.seasonal = seasonal
      self.stationary = stationary
      self.information_criterion = information_criterion
      self.test = test
      self.seasonal_test = seasonal_test
      self.stepwise = stepwise
      self.n_jobs = n_jobs
      self.trend = trend
      self.method = method
      self.offset_test_args = offset_test_args
      self.seasonal_test_args = seasonal_test_args
      self.trace = trace
      self.n_fits = n_fits
      self.with_intercept = with_intercept
      self.approximation = approximation
      self.truncate = truncate
      self.blambda = blambda
      self.biasadj = biasadj
      self.parallel = parallel
  
      super().__init__()
  
imports: from typing import Dict, List, Optional, Union
  from sktime.forecasting.base import BaseForecaster
  from sktime.forecasting.base.adapters._generalised_statsforecast import StatsForecastBackAdapter, _GeneralisedStatsForecastAdapter
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 1 to create a new StatsForecastAutoARIMA object from inherited base classes: _GeneralisedStatsForecastAdapter with example: obj = StatsForecastAutoARIMA(start_p, d, start_q, max_p, max_d, max_q, start_P, D, start_Q, max_P, max_D, max_Q, max_order, sp, seasonal, stationary, information_criterion, test, seasonal_test, stepwise, n_jobs, trend, method, offset_test_args, seasonal_test_args, trace, n_fits, with_intercept, approximation, truncate, blambda, biasadj, parallel)