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","."]
code: def set_params(self, **kwargs):
      self._parameters.update(kwargs)
      return self
  
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 3 for calling the MovingAveragePerturber obj's underlying member method code with example usage: obj.set_params() and returns: self