ExponentialSmoothing

class sktime.forecasting.exp_smoothing.ExponentialSmoothing(trend=None, damped_trend=False, seasonal=None, sp=None, initial_level=None, initial_trend=None, initial_seasonal=None, use_boxcox=None, initialization_method='estimated')[source]

Holt-Winters exponential smoothing forecaster. Default settings use simple exponential smoothing without trend and seasonality components.

Parameters
  • trend (str{"add", "mul", "additive", "multiplicative", None}, optional) –

  • (default=None) – Type of trend component.

  • damped_trend (bool, optional (default=None)) – Should the trend component be damped.

  • seasonal ({"add", "mul", "additive", "multiplicative", None}, optional) –

  • (default=None) – Type of seasonal component.

  • sp (int, optional (default=None)) – The number of seasonal periods to consider.

  • initial_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.

  • initial_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.

  • initial_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.

  • use_boxcox ({True, False, 'log', float}, optional) – 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.

References

[1] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles

and practice. OTexts, 2014.

__init__(trend=None, damped_trend=False, seasonal=None, sp=None, initial_level=None, initial_trend=None, initial_seasonal=None, use_boxcox=None, initialization_method='estimated')[source]

Initialize self. See help(type(self)) for accurate signature.