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

作者：Ismail, Shuhaida^*; Shabri, Ani; Samsudin, Ruhaidah

来源：Expert Systems with Applications, 2011, 38(8): 10574-10578.

DOI：10.1016/j.eswa.2011.02.107

• Time series
• Least square support vector machine
• Self-organizing maps
• 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 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.

• 出版日期2011-8