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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 저널정보
- 인하대학교 정석물류통상연구원 인하대학교 정석물류통상연구원 학술대회 Proceedings of the 3rd International conference on risk management &Global e-business(volume 2)
- 발행연도
- 2009.10
- 수록면
- 1,128 - 1,132 (5page)
이용수
초록· 키워드
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In this paper, a time series prediction approach by combing independent component
analysis (ICA) and support vector regression (SVR)is proposed ICA is a novel statistical signal processing technique that was originally proposed to find the latent source signals from observed mixture signal without knowing any prior knowledge of the mixing mechanism. SVR is and artificial intelligence forecasting technique and has been widely applied in time series prediction problems. The proposed approach First uers ICA
to the forecasting variables for generating the independent components(ICs). After identifying and removing the ICs containing the noise, the rest of the ICs are then used to reconstruct the forecasting variables which contain less noise. The SVR then uses the denoised forecasting variables to build the forecausting model. in order to evaluaate the performance of the proposed approach the TAIEX(Taiwan Stock Exchange Capitalization weighted Steock index) closing cash index is usde as the illusrtative example. Experimental results show that the proposed model outperforms the SVR model with mon ?filtered forecasting variables and random walk model
analysis (ICA) and support vector regression (SVR)is proposed ICA is a novel statistical signal processing technique that was originally proposed to find the latent source signals from observed mixture signal without knowing any prior knowledge of the mixing mechanism. SVR is and artificial intelligence forecasting technique and has been widely applied in time series prediction problems. The proposed approach First uers ICA
to the forecasting variables for generating the independent components(ICs). After identifying and removing the ICs containing the noise, the rest of the ICs are then used to reconstruct the forecasting variables which contain less noise. The SVR then uses the denoised forecasting variables to build the forecausting model. in order to evaluaate the performance of the proposed approach the TAIEX(Taiwan Stock Exchange Capitalization weighted Steock index) closing cash index is usde as the illusrtative example. Experimental results show that the proposed model outperforms the SVR model with mon ?filtered forecasting variables and random walk model
목차
Abstract
1. INTRODUCTION
3. PROPOSED PRODICTIONMODES
4. EXPERIMENTASL RESULTS
5. CONCLUSIONS
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UCI(KEPA) : I410-ECN-0101-2013-000-001296356