statsmodels.tsa.holtwinters.Holt.fit

Holt.fit(smoothing_level=None, smoothing_slope=None, damping_slope=None, optimized=True)[source]

fit Holt’s Exponential Smoothing wrapper(…)

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.
  • 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?
Returns:

results – See statsmodels.tsa.holtwinters.HoltWintersResults
Return type:

HoltWintersResults class

Notes

This is a full implementation of the holts exponential smoothing as per [1].

References

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