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')[source]

Holt Winter’s Exponential Smoothing

Parameters:

endog : array_like

Time series

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

Type of trend component.

damped : bool, optional

Should the trend component be damped.

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

Type of seasonal component.

seasonal_periods : int, 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:

results : ExponentialSmoothing class

Notes

This is a full implementation of the holt winters exponential smoothing as per [R107]. 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

[R107](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.