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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 615152, 20 pages
Research Article

A Trend-Based Segmentation Method and the Support Vector Regression for Financial Time Series Forecasting

Department of Information Management, Yuan Ze University, Taoyuan 32026, Taiwan

Received 10 February 2012; Accepted 28 March 2012

Academic Editor: Ming Li

Copyright © 2012 Jheng-Long Wu and Pei-Chann Chang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a novel trend-based segmentation method (TBSM) and the support vector regression (SVR) for financial time series forecasting. The model is named as TBSM-SVR. Over the last decade, SVR has been a popular forecasting model for nonlinear time series problem. The general segmentation method, that is, the piecewise linear representation (PLR), has been applied to locate a set of trading points within a financial time series data. However, owing to the dynamics in stock trading, PLR cannot reflect the trend changes within a specific time period. Therefore, a trend based segmentation method is developed in this research to overcome this issue. The model is tested using various stocks from America stock market with different trend tendencies. The experimental results show that the proposed model can generate more profits than other models. The model is very practical for real-world application, and it can be implemented in a real-time environment.