statsmodels.tsa.holtwinters.SimpleExpSmoothing

class statsmodels.tsa.holtwinters.SimpleExpSmoothing(endog, initialization_method=None, initial_level=None)

Simple Exponential Smoothing

Parameters:

endogarray_like

The time series to model.

initialization_methodstr, optional

Method for initialize the recursions. One of:

• None

• ‘estimated’

• ‘heuristic’

• ‘legacy-heuristic’

• ‘known’

None defaults to the pre-0.12 behavior where initial values are passed as part of fit. If any of the other values are passed, then the initial values must also be set when constructing the model. If ‘known’ initialization is used, then initial_level must be passed, as well as initial_trend and initial_seasonal if applicable. Default is ‘estimated’. “legacy-heuristic” uses the same values that were used in statsmodels 0.11 and earlier.

initial_levelfloat, optional

The initial level component. Required if estimation method is “known”. If set using either “estimated” or “heuristic” this value is used. This allows one or more of the initial values to be set while deferring to the heuristic for others or estimating the unset parameters.

Attributes:

endog_names

Names of endogenous variables

exog_names

The names of the exogenous variables.

Methods

fit([smoothing_level, optimized, ...])	Fit the model
fix_params(values)	Temporarily fix parameters for estimation
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([initial_level, ...])	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])	In-sample and out-of-sample prediction
score(params)	Score vector of model

See also

ExponentialSmoothing

Exponential smoothing with trend and seasonal components.

Holt

Exponential smoothing with a trend component.

Notes

This is a full implementation of the simple exponential smoothing as per [1]. SimpleExpSmoothing is a restricted version of ExponentialSmoothing.

See the notebook Exponential Smoothing for an overview.

References

[1]

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

Methods

fit([smoothing_level, optimized, ...])	Fit the model
fix_params(values)	Temporarily fix parameters for estimation
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([initial_level, ...])	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])	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.