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

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

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Official URL: http://dx.doi.org/10.1016/j.eswa.2011.02.107

Abstract

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.

Item Type:Article
Uncontrolled Keywords:forecasting, least square support vector machine, self-organizing maps, time series
Subjects:Q Science
Divisions:Science
ID Code:28591
Deposited By: Yanti Mohd Shah
Deposited On:25 Oct 2012 15:00
Last Modified:28 Jan 2019 11:35

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