statsmodels.tsa.holtwinters.ExponentialSmoothing

class statsmodels.tsa.holtwinters.ExponentialSmoothing(endog, trend=None, damped=False, seasonal=None, seasonal_periods=None, dates=None, freq=None, missing='none')

Holt Winter’s Exponential Smoothing

Parameters

endogarray-like

Time series

trend{“add”, “mul”, “additive”, “multiplicative”, None}, optional

Type of trend component.

dampedbool, optional

Should the trend component be damped.

seasonal{“add”, “mul”, “additive”, “multiplicative”, None}, optional

Type of seasonal component.

seasonal_periodsint, optional

The number of periods in a complete seasonal cycle, e.g., 4 for quarterly data or 7 for daily data with a weekly cycle.

Returns

resultsExponentialSmoothing class

Notes

This is a full implementation of the holt winters exponential smoothing as per [1]. This includes all the unstable methods as well as the stable methods. The implementation of the library covers the functionality of the R library as much as possible whilst still being Pythonic.

References

1(1,2)

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

Attributes

endog_names

Names of endogenous variables

exog_names

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.