statsmodels.tsa.holtwinters.Holt.fit

Holt.fit(smoothing_level=None, smoothing_trend=None, *, damping_trend=None, optimized=True, start_params=None, initial_level=None, initial_trend=None, use_brute=True, use_boxcox=None, remove_bias=False, method=None, minimize_kwargs=None)

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_trendfloat, 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_trendfloat, 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.

Deprecated since version 0.12: Set initial_level when constructing the model

initial_trendfloat, optional

Value to use when initializing the fitted trend.

Deprecated since version 0.12: Set initial_trend when constructing the model

use_brutebool, 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_biasbool, optional

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

methodstr, 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_kwargsdict[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:

HoltWintersResults

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.