class_docstr: Intermittent demand forecasting model using the Aggregate-Dissagregate Intermittent
  Demand Approach [Nikolopoulos2011]_.
  
  Based on `statsforecast.models.ADIDA <https://nixtla.mintlify.app/statsforecast/docs/models/adida.html>`_.
  
  
  References
  ----------
  .. [Nikolopoulos2011] Nikolopoulos, K., Syntetos, A., Boylan, J. et al. An aggregate–disaggregate
      intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis.
      J Oper Res Soc 62, 544–554 (2011). https://doi.org/10.1057/jors.2010.32
  
  
  Other Parameters
  ----------------
  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 _get_model_type(self):
      from statsforecast.models import ADIDA
  
      return ADIDA
  
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 ADIDAModel obj's underlying member method code with example usage: obj._get_model_type() and returns: ADIDA