Combining exponential smoothing forecasts using Akaike weights

Kolassa, Stephan (2011) Combining exponential smoothing forecasts using Akaike weights. International Journal of Forecasting, 27 (2). pp. 238-251. ISSN 0169-2070

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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.

Item Type:
Journal Article
Journal or Publication Title:
International Journal of Forecasting
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1400/1403
Subjects:
ID Code:
134025
Deposited By:
Deposited On:
22 Jun 2019 09:14
Refereed?:
Yes
Published?:
Published
Last Modified:
09 Sep 2020 06:02