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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Main Authors: | LI, Juan Cao, KOK, Seng Chua, LIM, Kian Guan |
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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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