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A combination selection algorithm on forecasting
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DOI：10.1016/j.ejor.2013.08.045.png)
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Abstract
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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. (C) 2013 Elsevier B.V. All rights reserved.
Keywords:
Neural networks
Seasonal autoregressive integrated moving average
Combination forecast
Information theory
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