A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting
A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting
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DOI：
10.1016/j.eswa.2011.02.107
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发表时间：
2011-08-01
影响因子：
8.5
通讯作者：
Samsudin, Ruhaidah
中科院分区：
文献类型：
--
作者：
Ismail, Shuhaida;Shabri, Ani;Samsudin, Ruhaidah
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. (C) 2011 Elsevier Ltd. All rights reserved.
