An Improved Support Vector Regression Modeling for Taiwan Stock Exchange Market Weighted Index Forecasting
Kuan-Yu Chen; Chia-Hui Ho
Neural Networks and Brain, 2005. ICNN&B apos;05. International Conference on
Volume 3, Issue , 13-15 Oct. 2005 Page(s):nil15 - 1638
Digital Object Identifier   10.1109/ICNNB.2005.1614944
Summary:This study applies a novel neural network technique, support vector regression (SVR), to Taiwan stock exchange market weighted index (TAIEX) forecasting. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as GA-SVR, which searches for SVR s optimal parameters using real value genetic algorithms. The experimental results demonstrate that SVR outperforms the ANN and RW models based on the normalized mean square error (NMSE), mean square error (MSE) and mean absolute percentage error (MAPE). Moreover, in order to test the importance and understand the features of SVR model, this study examines the effects of the number of input node

» View citation and abstract

Log in by entering your IEEE Web Account Username and Password.

IEEE Communications Society members: If you subscribe to the IEEE Electronic Periodicals Package or IEEE Electronic Periodicals Package Plus, you must access your subscription at www.comsoc.org.

Check with your librarian, information professional, or system manager to determine if you need to log in. Please complete the online Technical Support Form if you need assistance.

Select the Purchase History link to access the document. You will have 5 Days after purchase to access the Full Text PDF. Please complete the online Technical Support Form if you need assistance.

• Search and access Abstract records free of charge
• Register for table of contents alerts
• Purchase Full Text PDF documents

» Learn more about subscription options or how to become an IEEE Member.