class_docstr: Filter a times series using the Hodrick-Prescott filter.
  
  This is a wrapper around the `hpfilter` function from `statsmodels`.
  (see `statsmodels.tsa.filters.hp_filter.hpfilter`).
  
  Parameters
  ----------
  lamb : float
      The Hodrick-Prescott smoothing parameter. A value of 1600 is
      suggested for quarterly data. Ravn and Uhlig suggest using a value
      of 6.25 (1600/4**4) for annual data and 129600 (1600*3**4) for monthly
      data.
  
  Notes
  -----
  The HP filter removes a smooth trend
  ----------
  Hodrick, R.J, and E. C. Prescott. 1980. "Postwar U.S. Business Cycles: An
      Empirical Investigation." `Carnegie Mellon University discussion
      paper no. 451`.
  Ravn, M.O and H. Uhlig. 2002. "Notes On Adjusted the Hodrick-Prescott
      Filter for the Frequency of Observations." `The Review of Economics and
      Statistics`, 84(2), 371-80.
  
  Examples
  --------
  >>> from sktime.transformations.series.hpfilter import HPFilter # 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.period_range('1959Q1', '2009Q3', freq='Q') # doctest: +SKIP
  >>> dta.set_index(index, inplace=True) # doctest: +SKIP
  >>> hp = HPFilter(1600) # doctest: +SKIP
  >>> cycles = hp.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`
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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 = {"lamb": 1600}
      params2 = {}
      return [params1, params2]
  
imports: import pandas
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 3 for calling the HPFilter obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list