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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2011
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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 |
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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 |
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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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