statsmodels.tsa.holtwinters.ExponentialSmoothing.fit

ExponentialSmoothing.fit(smoothing_level=None, smoothing_slope=None, smoothing_seasonal=None, damping_slope=None, optimized=True, use_boxcox=False, remove_bias=False, use_basinhopping=False)

fit Holt Winter’s Exponential Smoothing

Parameters:

• 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_slope (float, optional) – The beta value of the holts 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_slope (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) – Should the values that have not been set above be optimized automatically?
• use_boxcox ({True, False, 'log', float}, optional) – Should the boxcox tranform be applied to the data first? If ‘log’ then apply the log. If float then use lambda equal to float.
• remove_bias (bool, optional) – Should the bias be removed from the forecast values and fitted values before being returned? Does this by enforcing average residuals equal to zero.
• use_basinhopping (bool, optional) – Should the opptimser try harder using basinhopping to find optimal values?

Returns:

results – See statsmodels.tsa.holtwinters.HoltWintersResults

Return type:

HoltWintersResults class

Notes

This is a full implementation of the holt winters exponential smoothing as per [1]. This includes all the unstable methods as well as the stable methods. The implementation of the library covers the functionality of the R library as much as possible whilst still being pythonic.

References

[1] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles and practice. OTexts, 2014.