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: | , , |
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| Format: | Article |
| Published: |
Elsevier Ltd.
2011
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| Subjects: | |
| 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 |
| 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. |
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