Title

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