A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting

Support vector machine is a new tool from Artificial Intelligence (AI) field has been successfully applied for a wide variety of problem especially in time-series forecasting. In this paper, least square support vector machine (LSSVM) is an improved algorithm based on SVM, with the combination of se...

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Main Authors: Ismail, Shuhaida, Shabri, Ani, Samsudin, Ruhaidah
Format: Article
Published: Elsevier Ltd. 2011
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Online Access:http://eprints.utm.my/id/eprint/28591/
http://dx.doi.org/10.1016/j.eswa.2011.02.107
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Institution: Universiti Teknologi Malaysia
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spelling my.utm.285912019-01-28T03:35:26Z http://eprints.utm.my/id/eprint/28591/ A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting Ismail, Shuhaida Shabri, Ani Samsudin, Ruhaidah Q Science Support vector machine is a new tool from Artificial Intelligence (AI) field has been successfully applied for a wide variety of problem especially in time-series forecasting. In this paper, least square support vector machine (LSSVM) is an improved algorithm based on SVM, with the combination of self-organizing maps(SOM) also known as SOM-LSSVM is proposed for time-series forecasting. The objective of this paper is to examine the flexibility of SOM-LSSVM by comparing it with a single LSSVM model. To assess the effectiveness of SOM-LSSVM model, two well-known datasets known as the Wolf yearly sunspot data and the Monthly unemployed young women data are used in this study. The experiment shows SOM-LSSVM outperforms the single LSSVM model based on the criteria of mean absolute error (MAE) and root mean square error (RMSE). It also indicates that SOM-LSSVM provides a promising alternative technique in time-series forecasting. Elsevier Ltd. 2011-08 Article PeerReviewed Ismail, Shuhaida and Shabri, Ani and Samsudin, Ruhaidah (2011) A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting. Expert Systems with Applications, 38 (8). pp. 10574-10578. ISSN 0957-4174 http://dx.doi.org/10.1016/j.eswa.2011.02.107 DOI:10.1016/j.eswa.2011.02.107
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic Q Science
spellingShingle Q Science
Ismail, Shuhaida
Shabri, Ani
Samsudin, Ruhaidah
A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting
description Support vector machine is a new tool from Artificial Intelligence (AI) field has been successfully applied for a wide variety of problem especially in time-series forecasting. In this paper, least square support vector machine (LSSVM) is an improved algorithm based on SVM, with the combination of self-organizing maps(SOM) also known as SOM-LSSVM is proposed for time-series forecasting. The objective of this paper is to examine the flexibility of SOM-LSSVM by comparing it with a single LSSVM model. To assess the effectiveness of SOM-LSSVM model, two well-known datasets known as the Wolf yearly sunspot data and the Monthly unemployed young women data are used in this study. The experiment shows SOM-LSSVM outperforms the single LSSVM model based on the criteria of mean absolute error (MAE) and root mean square error (RMSE). It also indicates that SOM-LSSVM provides a promising alternative technique in time-series forecasting.
format Article
author Ismail, Shuhaida
Shabri, Ani
Samsudin, Ruhaidah
author_facet Ismail, Shuhaida
Shabri, Ani
Samsudin, Ruhaidah
author_sort Ismail, Shuhaida
title A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting
title_short A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting
title_full A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting
title_fullStr A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting
title_full_unstemmed A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting
title_sort hybrid model of self-organizing maps (som) and least square support vector machine (lssvm) for time-series forecasting
publisher Elsevier Ltd.
publishDate 2011
url http://eprints.utm.my/id/eprint/28591/
http://dx.doi.org/10.1016/j.eswa.2011.02.107
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