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 seasons to consider for the holt winters.

Returns

results

Return type

ExponentialSmoothing 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] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles

and practice. OTexts, 2014.

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.

Attributes

endog_names

Names of endogenous variables

exog_names