statsmodels.tsa.holtwinters.SimpleExpSmoothing.fit

method

SimpleExpSmoothing.fit(smoothing_level=None, optimized=True, start_params=None, initial_level=None, use_brute=True)

Fit the model

Parameters

smoothing_levelfloat, optional

The smoothing_level value of the simple exponential smoothing, 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_params: array, 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.

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

resultsHoltWintersResults class

See statsmodels.tsa.holtwinters.HoltWintersResults

Notes

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

References

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

and practice. OTexts, 2014.