statsmodels.tsa.holtwinters.Holt.fit

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

Fit the model

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 Holt’s 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

Estimate model parameters by maximizing the log-likelihood

start_params : ndarray, optional

Starting values to used when optimizing the fit. If not provided, starting values are determined using a combination of grid search and reasonable values based on the initial values of the data

initial_level : float, optional

Value to use when initializing the fitted level.

initial_slope : float, optional

Value to use when initializing the fitted slope.

use_brute : bool, optional

Search for good starting values using a brute force (grid) optimizer. If False, a naive set of starting values is used.

Returns

results : HoltWintersResults class

See statsmodels.tsa.holtwinters.HoltWintersResults

Notes

This is a full implementation of the Holt’s exponential smoothing as per [1].

References

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

and practice. OTexts, 2014.