class_docstr: Croston's method for forecasting intermittent time series.
  
  Implements the method proposed by Croston in [1]_ and described in [2]_.
  
  Croston's method is a modification of (vanilla) exponential smoothing to handle
  intermittent time series. A time series is considered intermittent if many
  of its values are zero and the gaps between non-zero entries are not periodic.
  
  Croston's method will predict a constant value for all future times, so
  Croston's method essentially provides another notion for the average value
  of a time series.
  
  The method is (equivalent to) the following:
  
  - Let :math:`v_0,\ldots,v_n` be the non-zero values of the time series
  - Let :math:`v` be the exponentially smoothed average of :math:`v_0,\ldots,v_n`
  - Let :math:`z_0,\ldots,z_n` be the number of consecutive zeros plus 1 between
    the :math:`v_i` in the original time series.
  - Let :math:`z` be the exponentially smoothed average of :math:`z_0,\ldots,z_n`
  - Then the forecast is :math:`\frac{v}{z}`
  
  The intuition is that :math:`v` is a weighted average of the non-zero time
  series values and :math:`\frac{1}{z}` estimates the probability of getting a
  non-zero value.
  
  Example to illustrate the :math:`v` and :math:`z` notation.
  
  - If the original time series is :math:`0,0,2,7,0,0,0,-5` then:
  
      - The :math:`v`'s are :math:`2,7,-5`
      - The :math:`z`'s are :math:`3,1,4`
  
  Parameters
  ----------
  smoothing : float, default = 0.1
      Smoothing parameter in exponential smoothing
  
  Examples
  --------
  >>> from sktime.forecasting.croston import Croston
  >>> from sktime.datasets import load_PBS_dataset
  >>> y = load_PBS_dataset()
  >>> forecaster = Croston(smoothing=0.1)
  >>> forecaster.fit(y)
  Croston(...)
  >>> y_pred = forecaster.predict(fh=[1,2,3])
  
  See Also
  --------
  ExponentialSmoothing
  
  References
  ----------
  .. [1] J. D. Croston. Forecasting and stock control for intermittent demands.
     Operational Research Quarterly (1970-1977), 23(3):pp. 289–303, 1972.
  .. [2] N. Vandeput. Forecasting Intermittent Demand with the Croston Model.
     https://towardsdatascience.com/croston-forecast-model-for-intermittent-demand-360287a17f5f
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
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"]
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
      """
      params = [
          {},
          {"smoothing": 0},
          {"smoothing": 0.42},
          {"smoothing": 2},
      ]
  
      return params
  
file_path: sktime/sktime/forecasting/croston.py
imports: import numpy
  import pandas
  from sktime.forecasting.base import BaseForecaster
label_desc: Use image node_id 4 for calling the Croston obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: params