class_docstr: Use HampelFilter to detect outliers based on a sliding window.
  
  Correction of outliers is recommended by means of the sktime.Imputer,
  so both can be tuned separately.
  
  Parameters
  ----------
  window_length : int, optional (default=10)
      Length of the sliding window
  n_sigma : int, optional
      Defines how strong a point must outly to be an "outlier", by default 3
  k : float, optional
      A constant scale factor which is dependent on the distribution,
      for Gaussian it is approximately 1.4826, by default 1.4826
  return_bool : bool, optional
      If True, outliers are filled with True and non-outliers with False.
      Else, outliers are filled with np.nan.
  
  Notes
  -----
  Implementation is based on [1]_.
  
  References
  ----------
  .. [1] Hampel F. R., "The influence curve and its role in robust estimation",
     Journal of the American Statistical Association, 69, 382–393, 1974
  
  Examples
  --------
  >>> from sktime.transformations.series.outlier_detection import HampelFilter
  >>> from sktime.datasets import load_airline
  >>> y = load_airline()
  >>> transformer = HampelFilter(window_length=10)
  >>> y_hat = transformer.fit_transform(y)
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docstr: Return testing parameter settings for the estimator.
  
  Parameters
  ----------
  parameter_set : str, default="default"
      Name of the set of test parameters to return, for use in tests. If no
      special parameters are defined for a value, will return `"default"` set.
  
  
  Returns
  -------
  params : dict or list of dict, default = {}
      Parameters to create testing instances of the class
      Each dict are parameters to construct an "interesting" test instance, i.e.,
      `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
      `create_test_instance` uses the first (or only) dictionary in `params`
docstr_tok: ["Return","testing","parameter","settings","for","the","estimator",".","Parameters","--","--","--","--","--","parameter_set",":","str",",","default=","''","default","''","Name","of","the","set","of","test","parameters","to","return",",","for","use","in","tests",".","If","no","special","parameters","are","defined","for","a","value",",","will","return","`","``","default","''","`","set",".","Returns","--","--","--","-","params",":","dict","or","list","of","dict",",","default","=","{","}","Parameters","to","create","testing","instances","of","the","class","Each","dict","are","parameters","to","construct","an","``","interesting","''","test","instance",",","i.e.",",","`","MyClass","(","*","*","params",")","`","or","`","MyClass","(","*","*","params","[","i","]",")","`","creates","a","valid","test","instance",".","`","create_test_instance","`","uses","the","first","(","or","only",")","dictionary","in","`","params","`"]
code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the estimator.
  
      Parameters
      ----------
      parameter_set : str, default="default"
          Name of the set of test parameters to return, for use in tests. If no
          special parameters are defined for a value, will return `"default"` set.
  
  
      Returns
      -------
      params : dict or list of dict, default = {}
          Parameters to create testing instances of the class
          Each dict are parameters to construct an "interesting" test instance, i.e.,
          `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
          `create_test_instance` uses the first (or only) dictionary in `params`
      """
      return {"window_length": 3}
  
file_path: sktime/sktime/transformations/series/outlier_detection.py
imports: import warnings
  from math import ceil
  import numpy
  import pandas
  from sktime.split import SlidingWindowSplitter
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 4 for calling the HampelFilter obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: dict