Holt–Winters Forecasting: An Alternative Formulation Applied to UK Air Passenger Data

Abstract

This paper provides a formulation for the additive Holt–Winters forecasting procedure that simplifies both obtaining maximum likelihood estimates of all unknowns, smoothing parameters and initial conditions, and the computation of point forecasts and reliable predictive intervals. The stochastic component of the model is introduced by means of additive, uncorrelated, homoscedastic and Normal errors, and then the joint distribution of the data vector, a multivariate Normal distribution, is obtained. In the case where a data transformation was used to improve the fit of the model, cumulative forecasts are obtained here using a Monte-Carlo approximation. This paper describes the method by applying it to the series of monthly total UK air passengers collected by the Civil Aviation Authority, a long time series from 1949 to the present day, and compares the resulting forecasts with those obtained in previous studies.

Keywords:

• Exponential smoothing
• time series forecasting
• prediction intervals
• linear model
• additive error
• Monte-Carlo methods

Acknowledgements

The research leading to this paper was partially supported by the Ministerio de Educación Ciencia of Spain, Grant No. TIN2005-08404-C04-04.