# statsmodels.tsa.holtwinters.Holt¶

class statsmodels.tsa.holtwinters.Holt(endog, exponential=False, damped=False)[source]

Holt’s Exponential Smoothing

Parameters
endogarray_like

Time series

exponentialbool, optional

Type of trend component.

dampedbool, optional

Should the trend component be damped.

Returns
resultsHolt class

Notes

This is a full implementation of the Holt’s exponential smoothing as per [R10fa90404def-1]. Holt is a restricted version of ExponentialSmoothing.

References

R10fa90404def-1

Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles and practice. OTexts, 2014.

Methods

 fit([smoothing_level, smoothing_slope, …]) Fit the model from_formula(formula, data[, subset, drop_cols]) Create a Model from a formula and dataframe. hessian(params) The Hessian matrix of the model. information(params) Fisher information matrix of model. Compute initial values used in the exponential smoothing recursions Initialize (possibly re-initialize) a Model instance. loglike(params) Log-likelihood of model. predict(params[, start, end]) Returns in-sample and out-of-sample prediction. score(params) Score vector of model.

Properties

 endog_names Names of endogenous variables. exog_names The names of the exogenous variables.