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)

Fit the model

Parameters

smoothing_levelfloat, optional

The alpha value of the simple exponential smoothing, if the value is set then this value will be used as the value.

smoothing_slopefloat, 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_slopefloat, optional

The phi value of the damped method, if the value is set then this value will be used as the value.

optimizedbool, optional

Estimate model parameters by maximizing the log-likelihood

start_paramsndarray, 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_levelfloat, optional

Value to use when initializing the fitted level.

initial_slopefloat, optional

Value to use when initializing the fitted slope.

use_brutebool, optional

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

Returns

resultsHoltWintersResults 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.