A combination selection algorithm on forecasting

Cang, Shuang and Yu, Hongnian (2014) A combination selection algorithm on forecasting. European Journal of Operational Research, 234 (1). pp. 127-139. ISSN 0377-2217

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Official URL: http://dx.doi.org/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.

Item Type: Article
Uncontrolled Keywords: Neural networks; Seasonal autoregressive integrated moving average; Combination forecast; Information theory
Subjects: G900 Others in Mathematical and Computing Sciences
N100 Business studies
Department: Faculties > Business and Law > Newcastle Business School
Depositing User: Ellen Cole
Date Deposited: 11 Oct 2018 15:34
Last Modified: 19 Nov 2019 09:50
URI: http://nrl.northumbria.ac.uk/id/eprint/35251

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