class_docstr: Autoregressive Integrated Moving Average (ARIMA) model with fixed parameters.
  
  Based on `statsforecast.models.ARIMA <https://nixtla.mintlify.app/statsforecast/src/core/models.html#arima>`_.
  
  
  Other Parameters
  ----------------
  order: Tuple[int, int, int], default = (1, 1, 1)
      The (p, d, q) order of the model for the number of AR parameters, differences, and MA parameters to use.
  seasonal_order: Tuple[int, int, int], default = (0, 0, 0)
      The (P, D, Q) parameters of the seasonal ARIMA model. Setting to (0, 0, 0) disables seasonality.
  include_mean : bool, default = True
      Should the ARIMA model include a mean term?
  include_drift : bool, default = False
      Should the ARIMA model include a linear drift term?
  include_constant : bool, optional
      If True, then includ_mean is set to be True for undifferenced series and include_drift is set to be True for
      differenced series.
  blambda : float, optional
      Box-Cox transformation parameter.
  biasadj : bool, default = False
      Use adjusted back-transformed mean Box-Cox.
  method : {"CSS-ML", "CSS", "ML"}, default = "CSS-ML"
      Fitting method: CSS (conditional sum of squares), ML (maximum likelihood), CSS-ML (initialize with CSS, then
      optimize with ML).
  fixed : Dict[str, float], optional
      Dictionary containing fixed coefficients for the ARIMA model.
  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("order", (1, 1, 1))
      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 ARIMAModel obj's underlying member method code with example usage: obj._update_local_model_args(local_model_args) and returns: local_model_args