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
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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spelling sg-smu-ink.lkcsb_research-37812010-09-24T09:24:03Z Combining KPCA with Support Vector Machine for Time Series Forecasting LI, Juan Cao KOK, Seng Chua LIM, Kian Guan 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. 2003-03-20T08:00:00Z text https://ink.library.smu.edu.sg/lkcsb_research/2782 info:doi/10.1109/CIFER.2003.1196278 https://doi.org/10.1109/CIFER.2003.1196278 Research Collection Lee Kong Chian School Of Business eng Institutional Knowledge at Singapore Management University Finance and Financial Management Portfolio and Security Analysis
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Finance and Financial Management
Portfolio and Security Analysis
spellingShingle Finance and Financial Management
Portfolio and Security Analysis
LI, Juan Cao
KOK, Seng Chua
LIM, Kian Guan
Combining KPCA with Support Vector Machine for Time Series Forecasting
description 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.
format text
author LI, Juan Cao
KOK, Seng Chua
LIM, Kian Guan
author_facet LI, Juan Cao
KOK, Seng Chua
LIM, Kian Guan
author_sort LI, Juan Cao
title Combining KPCA with Support Vector Machine for Time Series Forecasting
title_short Combining KPCA with Support Vector Machine for Time Series Forecasting
title_full Combining KPCA with Support Vector Machine for Time Series Forecasting
title_fullStr Combining KPCA with Support Vector Machine for Time Series Forecasting
title_full_unstemmed Combining KPCA with Support Vector Machine for Time Series Forecasting
title_sort combining kpca with support vector machine for time series forecasting
publisher Institutional Knowledge at Singapore Management University
publishDate 2003
url https://ink.library.smu.edu.sg/lkcsb_research/2782
https://doi.org/10.1109/CIFER.2003.1196278
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