class_docstr: MovingAveragePerturber maintains the moving mean of the time series
  data as computed using specified window length, but add perturbed noise
  with similar distribution as the data with MA structure.
class_docstr_tok: ["MovingAveragePerturber","maintains","the","moving","mean","of","the","time","series","data","as","computed","using","specified","window","length",",","but","add","perturbed","noise","with","similar","distribution","as","the","data","with","MA","structure","."]
docstr: MovingAveragePerturber initialization
  
  Args:
      window_length (int): window length for mean and residual estimation. Defaults to 5.
      lag (int): lag parameter for MA noise generation. Defaults to 5.
      block_length (int): length of the contiguous time window (block) to perturb. Defaults to 5.
docstr_tok: ["MovingAveragePerturber","initialization","Args",":","window_length","(","int",")",":","window","length","for","mean","and","residual","estimation",".","Defaults","to","5.","lag","(","int",")",":","lag","parameter","for","MA","noise","generation",".","Defaults","to","5.","block_length","(","int",")",":","length","of","the","contiguous","time","window","(","block",")","to","perturb",".","Defaults","to","5","."]
code: def __init__(
      self,
      window_length: int = 5,
      lag: int = 5,
      block_length: int = 5,
  ):
      """
      MovingAveragePerturber initialization
  
      Args:
          window_length (int): window length for mean and residual estimation. Defaults to 5.
          lag (int): lag parameter for MA noise generation. Defaults to 5.
          block_length (int): length of the contiguous time window (block) to perturb. Defaults to 5.
      """
      super(MovingAveragePerturber, self).__init__()
      self._mean = None
      self._residual = None
      self._data_length = None
      self._parameters = dict()
      self._parameters["window_length"] = window_length
      self._parameters["lag"] = lag
      self._parameters["block_length"] = block_length
  
file_path: AIX360/aix360/algorithms/tsutils/tsperturbers/moving_average_perturber.py
imports: import numpy
  import pandas
  from typing import Union
  from aix360.algorithms.tsutils.tsframe import tsFrame
  from aix360.algorithms.tsutils.tsperturbers.tsperturber import BlockSelector, TSPerturber
  from aix360.algorithms.tsutils.tsperturbers.perturber_utils import ts_split_mean_residual
label_desc: Use image node_id 1 to create a new MovingAveragePerturber object from inherited base classes: TSPerturber with example: obj = MovingAveragePerturber(window_length, lag, block_length)