A Trend-Based Segmentation Method and the Support Vector Regression for Financial Time Series Forecasting
Joint Authors
Wu, Jheng-Long
Chang, Pei-Chann
Source
Mathematical Problems in Engineering
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-20, 20 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-05-29
Country of Publication
Egypt
No. of Pages
20
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Wu, Jheng-Long& Chang, Pei-Chann. 2012. A Trend-Based Segmentation Method and the Support Vector Regression for Financial Time Series Forecasting. Mathematical Problems in Engineering،Vol. 2012, no. 2012, pp.1-20.
https://search.emarefa.net/detail/BIM-1001759
Modern Language Association (MLA)
Wu, Jheng-Long& Chang, Pei-Chann. A Trend-Based Segmentation Method and the Support Vector Regression for Financial Time Series Forecasting. Mathematical Problems in Engineering No. 2012 (2012), pp.1-20.
https://search.emarefa.net/detail/BIM-1001759
American Medical Association (AMA)
Wu, Jheng-Long& Chang, Pei-Chann. A Trend-Based Segmentation Method and the Support Vector Regression for Financial Time Series Forecasting. Mathematical Problems in Engineering. 2012. Vol. 2012, no. 2012, pp.1-20.
https://search.emarefa.net/detail/BIM-1001759
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1001759