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A combination selection algorithm on forecasting.

Cang, S. and Yu, H., 2014. A combination selection algorithm on forecasting. European Journal of Operational Research, 234 (1), 127 - 139.

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DOI: 10.1016/j.ejor.2013.08.045


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. © 2013 Elsevier B.V. All rights reserved.

Item Type:Article
Uncontrolled Keywords:Combination forecast ; Information theory ; Neural networks ; Seasonal autoregressive integrated moving average
Group:Bournemouth University Business School
ID Code:22670
Deposited By: Symplectic RT2
Deposited On:14 Oct 2015 15:41
Last Modified:14 Mar 2022 13:53


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