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...
Saved in:
Main Authors: | , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2003
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/lkcsb_research/2782 https://doi.org/10.1109/CIFER.2003.1196278 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.lkcsb_research-3781 |
---|---|
record_format |
dspace |
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 |
_version_ |
1770570561091534848 |