statsmodels.tsa.holtwinters.SimpleExpSmoothing

class statsmodels.tsa.holtwinters.SimpleExpSmoothing(endog)

Simple Exponential Smoothing

Parameters:

endog : array_like

Time series

Returns:

results : SimpleExpSmoothing class

See also

ExponentialSmoothing, Holt

Notes

This is a full implementation of the simple exponential smoothing as per [R109]. SimpleExpSmoothing is a restricted version of ExponentialSmoothing.

References

[R109]

(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, optimized, …])	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, optimized, …])	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.