A combination selection algorithm on forecasting

Cang, S ORCID: https://orcid.org/0000-0002-7984-0728 and Yu, H, 2014. A combination selection algorithm on forecasting. European Journal of Operational Research, 234 (1), pp. 127-139. ISSN 0377-2217

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Abstract

It is widely accepted in forecasting that a combination model can improve forecasting accuracy. One important challenge is how to select the optimal subset of individual models from all available models without having to try all possible combinations of these models. This paper proposes an optimal subset selection algorithm from all individual models using information theory. The experimental results in tourism demand forecasting demonstrate that the combination of the individual models from the selected optimal subset significantly outperforms the combination of all available individual models. The proposed optimal subset selection algorithm provides a theoretical approach rather than experimental assessments which dominate literature.

Item Type:	Journal article
Publication Title:	European Journal of Operational Research
Creators:	Cang, S. and Yu, H.
Publisher:	Elsevier
Date:	1 April 2014
Volume:	234
Number:	1
ISSN:	0377-2217
Identifiers:	Number Type 10.1016/j.ejor.2013.08.045 DOI S0377221713007297 Publisher Item Identifier 1357047 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	27 Aug 2020 09:09
Last Modified:	31 May 2021 15:17
URI:	https://irep.ntu.ac.uk/id/eprint/40537

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