docstr: Naive Moving Average Model
  
  This model forecasts using an auto-regressive moving average (ARMA).
  
  Parameters
  ----------
  input_chunk_length
      The size of the sliding window used to calculate the moving average
  
  Examples
  --------
  >>> from darts.datasets import AirPassengersDataset
  >>> from darts.models import NaiveMovingAverage
  >>> series = AirPassengersDataset().load()
  # using the average of the last 6 months
  >>> model = NaiveMovingAverage(input_chunk_length=6)
  >>> pred = model.predict(6)
  >>> pred.values()
  array([[503.16666667],
         [483.36111111],
         [462.9212963 ],
         [455.40817901],
         [454.47620885],
         [465.22224366]])
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code: def __init__(self, input_chunk_length: int = 1):
      """Naive Moving Average Model
  
      This model forecasts using an auto-regressive moving average (ARMA).
  
      Parameters
      ----------
      input_chunk_length
          The size of the sliding window used to calculate the moving average
  
      Examples
      --------
      >>> from darts.datasets import AirPassengersDataset
      >>> from darts.models import NaiveMovingAverage
      >>> series = AirPassengersDataset().load()
      # using the average of the last 6 months
      >>> model = NaiveMovingAverage(input_chunk_length=6)
      >>> pred = model.predict(6)
      >>> pred.values()
      array([[503.16666667],
             [483.36111111],
             [462.9212963 ],
             [455.40817901],
             [454.47620885],
             [465.22224366]])
      """
      super().__init__()
      self.input_chunk_length = input_chunk_length
      self.rolling_window = None
  
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 NaiveMovingAverage object from inherited base classes: LocalForecastingModel with example: obj = NaiveMovingAverage(input_chunk_length)