Combining KPCA with Support Vector Machine for Time Series Forecasting
Publication Type
Conference Proceeding Article
Publication Date
3-2003
Abstract
Recently, support vector machine (SVM) has become a popular tool in time series forecasting. In developing a successful SVM forecaster, the first important step is feature extraction. This paper applies kernel principal component analysis (KPCA) to SVM for feature extraction. KPCA is a nonlinear PCA developed by using the kernel method. It firstly transforms the original inputs into a high dimensional feature space and then calculates PCA in the high dimensional feature space. By examining the sunspot data and one real futures contract, the experiment shows that SVM by feature forms much better than that extraction using KPCA per without feature extraction. In comparison with PCA, there is also superior performance in KPCA.
Discipline
Finance and Financial Management | Portfolio and Security Analysis
Research Areas
Finance
Publication
2003 IEEE International Conference on Computational Intelligence for Financial Engineering: Proceedings: March 20-23, 2003, Hong Kong
First Page
325
Last Page
329
ISBN
9780780376540
Identifier
10.1109/CIFER.2003.1196278
Publisher
IEEE
City or Country
Hong Kong
Citation
LI, Juan Cao; KOK, Seng Chua; and LIM, Kian Guan.
Combining KPCA with Support Vector Machine for Time Series Forecasting. (2003). 2003 IEEE International Conference on Computational Intelligence for Financial Engineering: Proceedings: March 20-23, 2003, Hong Kong. 325-329.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/2782