statsmodels.tsa.holtwinters.Holt

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

Holt’s Exponential Smoothing

Parameters:

endog : array_like

Time series

exponential : bool, optional

Type of trend component.

damped : bool, optional

Should the trend component be damped.
Returns:

results : Holt class

See also

ExponentialSmoothing, SimpleExpSmoothing

Notes

This is a full implementation of the Holt’s exponential smoothing as per [R108]. Holt is a restricted version of ExponentialSmoothing.

References

[R108](1, 2) Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles and practice. OTexts, 2014.

Attributes

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

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.

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.