class_docstr: Automatically tuned ARIMA model.
  
  Automatically selects the best (p,d,q,P,D,Q) model parameters using an information criterion
  
  Based on `statsforecast.models.AutoARIMA <https://nixtla.mintlify.app/statsforecast/docs/models/autoarima.html>`_.
  
  Other Parameters
  ----------------
  d : int, optional
      Order of first differencing. If None, will be determined automatically using a statistical test.
  D : int, optional
      Order of seasonal differencing. If None, will be determined automatically using a statistical test.
  max_p : int, default = 5
      Maximum number of autoregressive terms.
  max_q : int, default = 5
      Maximum order of moving average.
  max_P : int, default = 2
      Maximum number of seasonal autoregressive terms.
  max_Q : int, default = 2
      Maximum order of seasonal moving average.
  max_d : int, default = 2
      Maximum order of first differencing.
  max_D : int, default = 1
      Maximum order of seasonal differencing.
  start_p : int, default = 2
      Starting value of p in stepwise procedure.
  start_q : int, default = 2
      Starting value of q in stepwise procedure.
  start_P : int, default = 1
      Starting value of P in stepwise procedure.
  start_Q : int, default = 1
      Starting value of Q in stepwise procedure.
  stationary : bool, default = False
      Restrict search to stationary models.
  seasonal : bool, default = True
      Whether to consider seasonal models.
  approximation : bool, default = True
      Approximate optimization for faster convergence.
  allowdrift : bool, default = False
      If True, drift term is allowed.
  allowmean : bool, default = True
      If True, non-zero mean is allowed.
  seasonal_period : int or None, default = None
      Number of time steps in a complete seasonal cycle for seasonal models. For example, 7 for daily data with a
      weekly cycle or 12 for monthly data with an annual cycle.
      When set to None, seasonal_period will be inferred from the frequency of the training data. Can also be
      specified manually by providing an integer > 1.
      If seasonal_period (inferred or provided) is equal to 1, seasonality will be disabled.
  n_jobs : int or float, default = 0.5
      Number of CPU cores used to fit the models in parallel.
      When set to a float between 0.0 and 1.0, that fraction of available CPU cores is used.
      When set to a positive integer, that many cores are used.
      When set to -1, all CPU cores are used.
  max_ts_length : int, default = 2500
      If not None, only the last ``max_ts_length`` time steps of each time series will be used to train the model.
      This significantly speeds up fitting and usually leads to no change in accuracy.
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code: def _update_local_model_args(self, local_model_args: dict) -> dict:
      local_model_args = super()._update_local_model_args(
          local_model_args
      )
      local_model_args.setdefault("approximation", True)
      local_model_args.setdefault("allowmean", True)
      return local_model_args
  
imports: import logging
  from typing import Any, Dict, Type
  import numpy
  import pandas
  from .abstract_local_model import AbstractLocalModel
label_desc: Use image node_id 1 for calling the AutoARIMAModel obj's underlying member method code with example usage: obj._update_local_model_args(local_model_args) and returns: local_model_args