class_docstr: Theta forecasting model [Assimakopoulos2000]_.
  
  Based on `statsforecast.models.Theta <https://nixtla.mintlify.app/statsforecast/docs/models/autotheta.html>`_.
  
  
  References
  ----------
  .. [Assimakopoulos2000] Assimakopoulos, Vassilis, and Konstantinos Nikolopoulos.
      "The theta model: a decomposition approach to forecasting."
      International journal of forecasting 16.4 (2000): 521-530.
  
  
  Other Parameters
  ----------------
  decomposition_type : {"multiplicative", "additive"}, default = "multiplicative"
      Seasonal decomposition type.
  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 _get_model_type(self):
      from statsforecast.models import Theta
  
      return Theta
  
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 ThetaModel obj's underlying member method code with example usage: obj._get_model_type() and returns: Theta