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 Holt Winter’s Exponential Smoothing
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
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