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http://dx.doi.org/10.5391/JKIIS.2004.14.1.052

Fuzzy Support Vector Machine for Pattern Classification of Time Series Data of KOSPI200 Index  

Lee, S.Y. (연세대학교 컴퓨터정보공학부 및 BERC)
Sohn, S.Y. (연세대학교 컴퓨터산업공학부)
Kim, C.E. (연세대학교 응용통계학과)
Lee, Y.B. (연세대학교 컴퓨터정보공학부 및 BERC)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.14, no.1, 2004 , pp. 52-56 More about this Journal
Abstract
The Information of classification and estimate about KOSPI200 index`s up and down in the stock market becomes an important standard of decision-making in designing portofolio in futures and option market. Because the coming trend of time series patterns, an economic indicator, is very subordinate to the most recent economic pattern, it is necessary to study the recent patterns most preferentially. This paper compares classification and estimated performance of SVM(Support Vector Machine) and Fuzzy SVM model that are getting into the spotlight in time series analyses, neural net models and various fields. Specially, it proves that Fuzzy SVM is superior by presenting the most suitable dimension to fuzzy membership function that has time series attribute in accordance with learning Data Base.
Keywords
SYM; Fuzzy SVM; Time Series Analysis; KOSPI200; Stock Prediction; Pattern Classification;
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