docstr: Naive combination model
  
  Naive implementation of `EnsembleModel`
  Returns the average of all predictions of the constituent models
  
  If `future_covariates` or `past_covariates` are provided at training or inference time,
  they will be passed only to the models supporting them.
  
  Parameters
  ----------
  forecasting_models
      List of forecasting models whose predictions to ensemble
  train_forecasting_models
      Whether to train the `forecasting_models` from scratch. If `False`, the models are not trained when calling
      `fit()` and `predict()` can be called directly (only supported if all the `forecasting_models` are
      pretrained `GlobalForecastingModels`). Default: ``True``.
  show_warnings
      Whether to show warnings related to models covariates support.
  
  Examples
  --------
  >>> from darts.datasets import AirPassengersDataset
  >>> from darts.models import NaiveEnsembleModel, NaiveSeasonal, LinearRegressionModel
  >>> series = AirPassengersDataset().load()
  >>> # defining the ensemble
  >>> model = NaiveEnsembleModel([NaiveSeasonal(K=12), LinearRegressionModel(lags=4)])
  >>> model.fit(series)
  >>> pred = model.predict(6)
  >>> pred.values()
  array([[439.23152974],
         [431.41161602],
         [439.72888401],
         [453.70180806],
         [454.96757177],
         [485.16604194]])
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code: def __init__(
      self,
      forecasting_models: List[ForecastingModel],
      train_forecasting_models: bool = True,
      show_warnings: bool = True,
  ):
      """Naive combination model
  
      Naive implementation of `EnsembleModel`
      Returns the average of all predictions of the constituent models
  
      If `future_covariates` or `past_covariates` are provided at training or inference time,
      they will be passed only to the models supporting them.
  
      Parameters
      ----------
      forecasting_models
          List of forecasting models whose predictions to ensemble
      train_forecasting_models
          Whether to train the `forecasting_models` from scratch. If `False`, the models are not trained when calling
          `fit()` and `predict()` can be called directly (only supported if all the `forecasting_models` are
          pretrained `GlobalForecastingModels`). Default: ``True``.
      show_warnings
          Whether to show warnings related to models covariates support.
  
      Examples
      --------
      >>> from darts.datasets import AirPassengersDataset
      >>> from darts.models import NaiveEnsembleModel, NaiveSeasonal, LinearRegressionModel
      >>> series = AirPassengersDataset().load()
      >>> # defining the ensemble
      >>> model = NaiveEnsembleModel([NaiveSeasonal(K=12), LinearRegressionModel(lags=4)])
      >>> model.fit(series)
      >>> pred = model.predict(6)
      >>> pred.values()
      array([[439.23152974],
             [431.41161602],
             [439.72888401],
             [453.70180806],
             [454.96757177],
             [485.16604194]])
      """
      super().__init__(
          forecasting_models=forecasting_models,
          train_num_samples=1,
          train_samples_reduction=None,
          train_forecasting_models=train_forecasting_models,
          show_warnings=show_warnings,
      )
  
      # ensemble model initialised with trained global models can directly call predict()
      if self.all_trained and not train_forecasting_models:
          self._fit_called = True
  
imports: from typing import List, Optional, Sequence, Union
  import numpy
  from darts.logging import get_logger, raise_if, raise_if_not
  from darts.models.forecasting.ensemble_model import EnsembleModel
  from darts.models.forecasting.forecasting_model import ForecastingModel, LocalForecastingModel
  from darts.timeseries import TimeSeries
label_desc: Use image node_id 1 to create a new NaiveEnsembleModel object from inherited base classes: EnsembleModel with example: obj = NaiveEnsembleModel(forecasting_models, train_forecasting_models, show_warnings)