class_docstr: Wrapper for ``pmdarima.arima.AutoARIMA``.
  It currently does not handle the regressor issue when there is
  gap between train and predict periods.
  
  Parameters
  ----------
  score_func : callable
      see ``BaseForecastEstimator``.
  coverage : float between [0.0, 1.0]
      see ``BaseForecastEstimator``.
  null_model_params : dict with arguments to define DummyRegressor null model, optional, default=None
      see ``BaseForecastEstimator``.
  regressor_cols: `list` [`str`], optional, default None
      A list of regressor columns used during training and prediction.
      If None, no regressor columns are used.
  
  See ``AutoArima`` documentation for rest of the parameter descriptions:
  
          * https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.AutoARIMA.html#pmdarima.arima.AutoARIMA
  
  Attributes
  ----------
  model : ``AutoArima`` object
      Auto arima model object
  fit_df : `pandas.DataFrame` or None
      The training data used to fit the model.
  forecast : `pandas.DataFrame`
      Output of the predict method of ``AutoArima``.
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code: def __init__(
      self,
      # Null model parameters
      score_func: callable = mean_squared_error,
      coverage: float = 0.90,
      null_model_params: Optional[Dict] = None,
      # Additional parameters
      regressor_cols: Optional[List[str]] = None,
      freq: Optional[float] = None,
      # pmdarima fit parameters
      start_p: Optional[int] = 2,
      d: Optional[int] = None,
      start_q: Optional[int] = 2,
      max_p: Optional[int] = 5,
      max_d: Optional[int] = 2,
      max_q: Optional[int] = 5,
      start_P: Optional[int] = 1,
      D: Optional[int] = None,
      start_Q: Optional[int] = 1,
      max_P: Optional[int] = 2,
      max_D: Optional[int] = 1,
      max_Q: Optional[int] = 2,
      max_order: Optional[int] = 5,
      m: Optional[int] = 1,
      seasonal: Optional[bool] = True,
      stationary: Optional[bool] = False,
      information_criterion: Optional[str] = "aic",
      alpha: Optional[int] = 0.05,
      test: Optional[str] = "kpss",
      seasonal_test: Optional[str] = "ocsb",
      stepwise: Optional[bool] = True,
      n_jobs: Optional[int] = 1,
      start_params: Optional[Dict] = None,
      trend: Optional[str] = None,
      method: Optional[str] = "lbfgs",
      maxiter: Optional[int] = 50,
      offset_test_args: Optional[Dict] = None,
      seasonal_test_args: Optional[Dict] = None,
      suppress_warnings: Optional[bool] = True,
      error_action: Optional[str] = "trace",
      trace: Optional[Union[int, bool]] = False,
      random: Optional[bool] = False,
      random_state: Optional[Union[int, callable]] = None,
      n_fits: Optional[int] = 10,
      out_of_sample_size: Optional[int] = 0,
      scoring: Optional[str] = "mse",
      scoring_args: Optional[Dict] = None,
      with_intercept: Optional[Union[bool, str]] = "auto",
      # pmdarima predict parameters
      return_conf_int: Optional[bool] = True,
      dynamic: Optional[bool] = False,
  ):
      # Every subclass of BaseForecastEstimator must call super().__init__
      super().__init__(
          score_func=score_func,
          coverage=coverage,
          null_model_params=null_model_params,
      )
      self.regressor_cols = regressor_cols
      self.freq = freq
      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.m = m
      self.seasonal = seasonal
      self.stationary = stationary
      self.information_criterion = information_criterion
      self.alpha = alpha
      self.test = test
      self.seasonal_test = seasonal_test
      self.stepwise = stepwise
      self.n_jobs = n_jobs
      self.start_params = start_params
      self.trend = trend
      self.method = method
      self.maxiter = maxiter
      self.offset_test_args = offset_test_args
      self.seasonal_test_args = seasonal_test_args
      self.suppress_warnings = suppress_warnings
      self.error_action = error_action
      self.trace = trace
      self.random = random
      self.random_state = random_state
      self.n_fits = n_fits
      self.out_of_sample_size = out_of_sample_size
      self.scoring = scoring
      self.scoring_args = scoring_args
      self.with_intercept = with_intercept
      self.return_conf_int = return_conf_int
      self.coverage = coverage
      self.dynamic = dynamic
  
      # set by the fit method
      self.model = None
      self.fit_df = None
      # set by the predict method
      self.forecast = None
  
imports: from typing import Dict
  from typing import List
  from typing import Optional
  from typing import Union
  import numpy
  import pandas
  from pmdarima.arima import AutoARIMA
  from sklearn.metrics import mean_squared_error
  from greykite.common.constants import PREDICTED_COL
  from greykite.common.constants import PREDICTED_LOWER_COL
  from greykite.common.constants import PREDICTED_UPPER_COL
  from greykite.common.constants import TIME_COL
  from greykite.common.constants import VALUE_COL
  from greykite.common.time_properties import infer_freq
  from greykite.sklearn.estimator.base_forecast_estimator import BaseForecastEstimator
label_desc: Use image node_id 1 to create a new AutoArimaEstimator object from inherited base classes: BaseForecastEstimator with example: obj = AutoArimaEstimator(score_func, coverage, null_model_params, regressor_cols, freq, start_p, d, start_q, max_p, max_d, max_q, start_P, D, start_Q, max_P, max_D, max_Q, max_order, m, seasonal, stationary, information_criterion, alpha, test, seasonal_test, stepwise, n_jobs, start_params, trend, method, maxiter, offset_test_args, seasonal_test_args, suppress_warnings, error_action, trace, random, random_state, n_fits, out_of_sample_size, scoring, scoring_args, with_intercept, return_conf_int, dynamic)