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 [1]. Holt is a restricted version of ExponentialSmoothing.

References

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.

initial_values()

Compute initial values used in the exponential smoothing recursions

initialize()

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.