class_docstr: Filter a times series using the Baxter-King filter.
  
  This is a wrapper around the `bkfilter` function from `statsmodels`.
  (see `statsmodels.tsa.filters.bk_filter.bkfilter`).
  
  The Baxter-King filter is intended for economic and econometric time series
  data and deals with the periodicity of the business cycle. Applying their
  band-pass filter to a series will produce a new series that does not contain
  fluctuations at a higher or lower frequency than those of the business cycle.
  Baxter-King follow Burns and Mitchell's work on business cycles, which suggests
  that U.S. business cycles typically last from 1.5 to 8 years.
  
  Parameters
  ----------
  low : float
      Minimum period for oscillations. Baxter and King recommend a value of 6
      for quarterly data and 1.5 for annual data.
  
  high : float
      Maximum period for oscillations. BK recommend 32 for U.S. business cycle
      quarterly data and 8 for annual data.
  
  K : int
      Lead-lag length of the filter. Baxter and King suggest a truncation
      length of 12 for quarterly data and 3 for annual data.
  
  Notes
  -----
  Returns a centered weighted moving average of the original series.
  
  References
  ----------
  Baxter, M. and R. G. King. "Measuring Business Cycles: Approximate
      Band-Pass Filters for Economic Time Series." *Review of Economics and
      Statistics*, 1999, 81(4), 575-593.
  
  Examples
  --------
  >>> from sktime.transformations.series.bkfilter import BKFilter # doctest: +SKIP
  >>> import pandas as pd # doctest: +SKIP
  >>> import statsmodels.api as sm # doctest: +SKIP
  >>> dta = sm.datasets.macrodata.load_pandas().data # doctest: +SKIP
  >>> index = pd.date_range(start='1959Q1', end='2009Q4', freq='Q') # doctest: +SKIP
  >>> dta.set_index(index, inplace=True) # doctest: +SKIP
  >>> bk = BKFilter(6, 24, 12) # doctest: +SKIP
  >>> cycles = bk.fit_transform(X=dta[['realinv']]) # doctest: +SKIP
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.
      There are currently no reserved values for transformers.
  
  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",".","There","are","currently","no","reserved","values","for","transformers",".","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.
          There are currently no reserved values for transformers.
  
      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`
      """
      params1 = {"low": 5, "high": 24, "K": 4}
      params2 = {}
      return [params1, params2]
  
imports: import pandas
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 3 for calling the BKFilter obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list