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Combining exponential smoothing forecasts using Akaike weights

Research output: Contribution to journalJournal articlepeer-review

Published
<mark>Journal publication date</mark>1/04/2011
<mark>Journal</mark>International Journal of Forecasting
Issue number2
Volume27
Number of pages14
Pages (from-to)238-251
Publication StatusPublished
Early online date9/07/10
<mark>Original language</mark>English

Abstract

Simple forecast combinations such as medians and trimmed or winsorized means are known to improve the accuracy of point forecasts, and Akaike’s Information Criterion (AIC) has given rise to so-called Akaike weights, which have been used successfully to combine statistical models for inference and prediction in specialist fields, e.g., ecology and medicine. We examine combining exponential smoothing point and interval forecasts using weights derived from AIC, small-sample-corrected AIC and BIC on the M1 and M3 Competition datasets. Weighted forecast combinations perform better than forecasts selected using information criteria, in terms of both point forecast accuracy and prediction interval coverage. Simple combinations and weighted combinations do not consistently outperform one another, while simple combinations sometimes perform worse than single forecasts selected by information criteria. We find a tendency for a longer history to be associated with a better prediction interval coverage.