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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Bibliographic Details
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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Summary: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.