Combining KPCA with Support Vector Machine for Time Series Forecasting

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...

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Bibliographic Details
Main Authors: LI, Juan Cao, KOK, Seng Chua, LIM, Kian Guan
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2003
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Online Access:https://ink.library.smu.edu.sg/lkcsb_research/2782
https://doi.org/10.1109/CIFER.2003.1196278
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Institution: Singapore Management University
Language: English

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