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)[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_trend (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_trend (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.

    Deprecated since version 0.12: Set initial_level when constructing the model

  • initial_trend (float, optional) –

    Value to use when initializing the fitted trend.

    Deprecated since version 0.12: Set initial_trend when constructing the model

  • 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 Holt’s exponential smoothing as per [1].

References

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

and practice. OTexts, 2014.