statsmodels.tsa.holtwinters.SimpleExpSmoothing.fit¶

SimpleExpSmoothing.fit(smoothing_level=None, *, optimized=True, start_params=None, initial_level=None, use_brute=True, use_boxcox=None, remove_bias=False, method=None, minimize_kwargs=None)[source]¶

Fit the model

Parameters:¶

smoothing_level : float, optional¶

The smoothing_level value of the simple exponential smoothing, 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.

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.

use_boxcox : {True, False, 'log', float}, optional¶

Should the Box-Cox transform be applied to the data first? If ‘log’ then apply the log. If float then use the value as lambda.

remove_bias : bool, optional¶

Remove bias from forecast values and fitted values by enforcing that the average residual is equal to zero.

method : str, default "L-BFGS-B"¶

The minimizer used. Valid options are “L-BFGS-B” (default), “TNC”, “SLSQP”, “Powell”, “trust-constr”, “basinhopping” (also “bh”) and “least_squares” (also “ls”). basinhopping tries multiple starting values in an attempt to find a global minimizer in non-convex problems, and so is slower than the others.

minimize_kwargs : dict[str, Any]¶

A dictionary of keyword arguments passed to SciPy’s minimize function if method is one of “L-BFGS-B” (default), “TNC”, “SLSQP”, “Powell”, or “trust-constr”, or SciPy’s basinhopping or least_squares. The valid keywords are optimizer specific. Consult SciPy’s documentation for the full set of options.

Returns:¶

See statsmodels.tsa.holtwinters.HoltWintersResults.

Return type:¶

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.