Combining KPCA with Support Vector Machine for Time Series Forecasting

• Main Authors: LI, Juan Cao, KOK, Seng Chua, LIM, Kian Guan
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2003
• Subjects: Finance and Financial Management; Portfolio and Security Analysis
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/2782; https://doi.org/10.1109/CIFER.2003.1196278
• Institution: Singapore Management University

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...