class_docstr: Results from fitting Exponential Smoothing models.
  
  Parameters
  ----------
  model : ExponentialSmoothing instance
      The fitted model instance.
  params : dict
      All the parameters for the Exponential Smoothing model.
  sse : float
      The sum of squared errors.
  aic : float
      The Akaike information criterion.
  aicc : float
      AIC with a correction for finite sample sizes.
  bic : float
      The Bayesian information criterion.
  optimized : bool
      Flag indicating whether the model parameters were optimized to fit
      the data.
  level : ndarray
      An array of the levels values that make up the fitted values.
  trend : ndarray
      An array of the trend values that make up the fitted values.
  season : ndarray
      An array of the seasonal values that make up the fitted values.
  params_formatted : pd.DataFrame
      DataFrame containing all parameters, their short names and a flag
      indicating whether the parameter's value was optimized to fit the data.
  resid : ndarray
      An array of the residuals of the fittedvalues and actual values.
  k : int
      The k parameter used to remove the bias in AIC, BIC etc.
  fittedvalues : ndarray
      An array of the fitted values. Fitted by the Exponential Smoothing
      model.
  fittedfcast : ndarray
      An array of both the fitted values and forecast values.
  fcastvalues : ndarray
      An array of the forecast values forecast by the Exponential Smoothing
      model.
  mle_retvals : {None, scipy.optimize.optimize.OptimizeResult}
      Optimization results if the parameters were optimized to fit the data.
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docstr: An array of the forecast values
docstr_tok: ["An","array","of","the","forecast","values"]
code: def fcastvalues(self):
      """
      An array of the forecast values
      """
      return self._fcastvalues
  
imports: import numpy
  import pandas
  from scipy.special import inv_boxcox
  from scipy.stats import boxcox, rv_continuous, rv_discrete
  from scipy.stats.distributions import rv_frozen
  from statsmodels.base.data import PandasData
  from statsmodels.base.model import Results
  from statsmodels.base.wrapper import ResultsWrapper, populate_wrapper, union_dicts
label_desc: Use image node_id 15 for calling the HoltWintersResults obj's underlying member method code with example usage: obj.fcastvalues() and returns: self