statsmodels.tsa.holtwinters.Holt

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

Holt’s Exponential Smoothing wrapper(…)

Parameters:	endog (array-like) – Time series expoential (bool, optional) – Type of trend component. damped (bool, optional) – Should the trend component be damped.
Returns:	results
Return type:	Holt class

Notes

This is a full implementation of the holts exponential smoothing as per [1].

See also

Exponential, Simple

References

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

Methods

fit([smoothing_level, smoothing_slope, …])	fit Holt’s Exponential Smoothing wrapper(…)
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
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.

Attributes

endog_names	Names of endogenous variables
exog_names