class_docstr: Holt-Winters exponential smoothing forecaster.
  
  Direct interface for `statsmodels.tsa.holtwinters`.
  
  Default settings use simple exponential smoothing without trend and
  seasonality components.
  
  Parameters
  ----------
  trend : {"add", "mul", "additive", "multiplicative", None}, default=None
      Type of trend component.
  damped_trend : bool, default=False
      Should the trend component be damped.
  seasonal : {"add", "mul", "additive", "multiplicative", None}, default=None
      Type of seasonal component.Takes one of
  sp : int or None, default=None
      The number of seasonal periods to consider.
  initial_level : float or None, default=None
      The alpha value of the simple exponential smoothing, if the value
      is set then this value will be used as the value.
  initial_trend : float or None, default=None
      The beta value of the Holt's trend method, if the value is
      set then this value will be used as the value.
  initial_seasonal : float or None, default=None
      The gamma value of the holt winters seasonal method, if the value
      is set then this value will be used as the value.
  use_boxcox : {True, False, 'log', float}, default=None
      Should the Box-Cox transform be applied to the data first?
      If 'log' then apply the log. If float then use lambda equal to float.
  initialization_method:{'estimated','heuristic','legacy-heuristic','known',None},
      default='estimated'
      Method for initialize the recursions.
      If 'known' initialization is used, then `initial_level` must be
      passed, as well as `initial_trend` and `initial_seasonal` if
      applicable.
      'heuristic' uses a heuristic based on the data to estimate initial
      level, trend, and seasonal state. 'estimated' uses the same heuristic
      as initial guesses, but then estimates the initial states as part of
      the fitting process.
  smoothing_level : float, optional
      The alpha value of the simple exponential smoothing, if the value
      is set then this value will be used as the value.
  smoothing_trend :  float, optional
      The beta value of the Holt's trend method, if the value is
      set then this value will be used as the value.
  smoothing_seasonal : float, optional
      The gamma value of the holt winters seasonal method, if the value
      is set then this value will be used as the value.
  damping_trend : float, optional
      The phi value of the damped method, if the value is
      set then this value will be used as the value.
  optimized : bool, optional
      Estimate model parameters by maximizing the log-likelihood.
  remove_bias : bool, optional
      Remove bias from forecast values and fitted values by enforcing
      that the average residual is equal to zero.
  start_params : array_like, optional
      Starting values to used when optimizing the fit.  If not provided,
      starting values are determined using a combination of grid search
      and reasonable values based on the initial values of the data. See
      the notes for the structure of the model parameters.
  method : str, default "L-BFGS-B"
      The minimizer used. Valid options are "L-BFGS-B" , "TNC",
      "SLSQP" (default), "Powell", "trust-constr", "basinhopping" (also
      "bh") and "least_squares" (also "ls"). basinhopping tries multiple
      starting values in an attempt to find a global minimizer in
      non-convex problems, and so is slower than the others.
  minimize_kwargs : dict[str, Any]
      A dictionary of keyword arguments passed to SciPy's minimize
      function if method is one of "L-BFGS-B", "TNC",
      "SLSQP", "Powell", or "trust-constr", or SciPy's basinhopping
      or least_squares functions. The valid keywords are optimizer
      specific. Consult SciPy's documentation for the full set of
      options.
  use_brute : bool, optional
      Search for good starting values using a brute force (grid)
      optimizer. If False, a naive set of starting values is used.
  random_state : int, RandomState instance or None, optional ,
      default=None – If int, random_state is the seed used by the random
      number generator; If RandomState instance, random_state is the random
      number generator; If None, the random number generator is the
      RandomState instance used by np.random.
  
  References
  ----------
  [1] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles
      and practice. OTexts, 2014.
  
  Examples
  --------
  >>> from sktime.datasets import load_airline
  >>> from sktime.forecasting.exp_smoothing import ExponentialSmoothing
  >>> y = load_airline()
  >>> forecaster = ExponentialSmoothing(
  ...     trend='add', seasonal='multiplicative', sp=12
  ... )  # doctest: +SKIP
  >>> forecaster.fit(y)  # doctest: +SKIP
  ExponentialSmoothing(...)
  >>> y_pred = forecaster.predict(fh=[1,2,3])  # doctest: +SKIP
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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.
      There are currently no reserved values for forecasters.
  
  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","forecasters",".","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 forecasters.
  
      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
      """
      params = [
          {},
          {
              "trend": "mul",
              "damped_trend": True,
              "seasonal": "mul",
              "sp": 2,
              "use_boxcox": False,
              "initialization_method": "heuristic",
              "smoothing_level": 0.1,
              "smoothing_trend": 0.1,
              "damping_trend": 0.42,
              "method": "least_squares",
          },
          {
              "trend": "add",
              "damped_trend": False,
              "seasonal": "add",
              "sp": 2,
              "use_boxcox": True,
              "initialization_method": "estimated",
              "smoothing_level": 0.3,
              "smoothing_trend": 0.5,
              "damping_trend": 0.28,
              "method": "SLSQP",
          },
      ]
  
      return params
  
imports: from sktime.forecasting.base.adapters import _StatsModelsAdapter
label_desc: Use image node_id 3 for calling the ExponentialSmoothing obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: params