sktime.forecasting.exp_smoothing¶
-
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]¶ Bases:
sktime.forecasting.base.adapters._statsmodels._StatsModelsAdapter
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.