Improving weighted information criterion by using optimization

Aladag, Cagdas Hakan and Egrioglu, Erol and Gunay, Suleyman and Basaran, Murat A. (2010) Improving weighted information criterion by using optimization. Journal of Computational and Applied Mathematics, 233 (10). pp. 2683-2687. ISSN 0377-0427

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Abstract

Although artificial neural networks (ANN) have been widely used in forecasting time series, the determination of the best model is still a problem that has been studied a lot. Various approaches available in the literature have been proposed in order to select the best model for forecasting in ANN in recent years. One of these approaches is to use a model selection strategy based on the weighted information criterion (WIC). WIC is calculated by summing weighted different selection criteria which measure the forecasting accuracy of an ANN model in different ways. In the calculation of WIC, the weights of different selection criteria are determined heuristically. In this study, these weights are calculated by using optimization in order to obtain a more consistent criterion. Four real time series are analyzed in order to show the efficiency of the improved WIC. When the weights are determined based on the optimization, it is obviously seen that the improved WIC produces better results.

Item Type: Journal Article
Journal or Publication Title: Journal of Computational and Applied Mathematics
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/2600/2604
Subjects:
Departments: Lancaster University Management School > Management Science
Faculty of Science and Technology > Mathematics and Statistics
ID Code: 139564
Deposited By: ep_importer_pure
Deposited On: 13 Dec 2019 16:05
Refereed?: Yes
Published?: Published
Last Modified: 14 Feb 2020 06:03
URI: https://eprints.lancs.ac.uk/id/eprint/139564

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