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Improving weighted information criterion by using optimization

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Improving weighted information criterion by using optimization. / Aladag, Cagdas Hakan; Egrioglu, Erol; Gunay, Suleyman et al.
In: Journal of Computational and Applied Mathematics, Vol. 233, No. 10, 15.03.2010, p. 2683-2687.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Aladag, CH, Egrioglu, E, Gunay, S & Basaran, MA 2010, 'Improving weighted information criterion by using optimization', Journal of Computational and Applied Mathematics, vol. 233, no. 10, pp. 2683-2687. https://doi.org/10.1016/j.cam.2009.11.016

APA

Aladag, C. H., Egrioglu, E., Gunay, S., & Basaran, M. A. (2010). Improving weighted information criterion by using optimization. Journal of Computational and Applied Mathematics, 233(10), 2683-2687. https://doi.org/10.1016/j.cam.2009.11.016

Vancouver

Aladag CH, Egrioglu E, Gunay S, Basaran MA. Improving weighted information criterion by using optimization. Journal of Computational and Applied Mathematics. 2010 Mar 15;233(10):2683-2687. doi: 10.1016/j.cam.2009.11.016

Author

Aladag, Cagdas Hakan ; Egrioglu, Erol ; Gunay, Suleyman et al. / Improving weighted information criterion by using optimization. In: Journal of Computational and Applied Mathematics. 2010 ; Vol. 233, No. 10. pp. 2683-2687.

Bibtex

@article{fcf06d521ef9458a875fb908a66f4aed,
title = "Improving weighted information criterion by using optimization",
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.",
keywords = "Artificial neural networks, Consistency, Forecasting, Model selection, Time series, Weighted information criterion",
author = "Aladag, {Cagdas Hakan} and Erol Egrioglu and Suleyman Gunay and Basaran, {Murat A.}",
year = "2010",
month = mar,
day = "15",
doi = "10.1016/j.cam.2009.11.016",
language = "English",
volume = "233",
pages = "2683--2687",
journal = "Journal of Computational and Applied Mathematics",
issn = "0377-0427",
publisher = "Elsevier",
number = "10",

}

RIS

TY - JOUR

T1 - Improving weighted information criterion by using optimization

AU - Aladag, Cagdas Hakan

AU - Egrioglu, Erol

AU - Gunay, Suleyman

AU - Basaran, Murat A.

PY - 2010/3/15

Y1 - 2010/3/15

N2 - 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.

AB - 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.

KW - Artificial neural networks

KW - Consistency

KW - Forecasting

KW - Model selection

KW - Time series

KW - Weighted information criterion

U2 - 10.1016/j.cam.2009.11.016

DO - 10.1016/j.cam.2009.11.016

M3 - Journal article

AN - SCOPUS:73449108019

VL - 233

SP - 2683

EP - 2687

JO - Journal of Computational and Applied Mathematics

JF - Journal of Computational and Applied Mathematics

SN - 0377-0427

IS - 10

ER -