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題名 An empirical study of applying data mining techniques to the prediction of TAIEX Futures
作者 Lin, H.-C.;Hsu, Kuo-Wei
徐國偉
貢獻者 資科系
關鍵詞 Binary classification problems; Data preprocessing; Data stream mining; Empirical studies; futures; Real-world problem; Stock index futures; Taiwan stock exchanges; Classification (of information); Data communication systems; Forecasting; Granular computing; Data mining
日期 2012
上傳時間 10-四月-2015 16:38:26 (UTC+8)
摘要 It is an inevitable trend to learn and extract useful knowledge from massive data, so that data miming has been one of popular fields for researches and practitioners. Recently, data stream mining has emerged as an important subfield of data mining, because data samples usually are generated in a sequence over time and collected in a form of a stream in many cases in the real world. In this paper, we study a real-world problem and apply data stream mining techniques to the prediction of Taiwan Stock Exchange Capitalization Weighted Stock Index Futures (TAIEX Futures). We model the problem as a binary classification problem and our goal is to predict the rising or falling of the short-term futures. We design the data pre-processing procedure and employ a data stream miming toolkit in experiments. The results indicate that the concept drift detection method is helpful for TAIEX Futures in which concept drift supposedly exists and also that data stream mining technology is helpful for predicting the futures market. © 2012 IEEE.
關聯 Proceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012
10.1109/GrC.2012.6468567
資料類型 conference
DOI http://dx.doi.org/10.1109/GrC.2012.6468567
dc.contributor 資科系
dc.creator (作者) Lin, H.-C.;Hsu, Kuo-Wei
dc.creator (作者) 徐國偉zh_TW
dc.date (日期) 2012
dc.date.accessioned 10-四月-2015 16:38:26 (UTC+8)-
dc.date.available 10-四月-2015 16:38:26 (UTC+8)-
dc.date.issued (上傳時間) 10-四月-2015 16:38:26 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/74478-
dc.description.abstract (摘要) It is an inevitable trend to learn and extract useful knowledge from massive data, so that data miming has been one of popular fields for researches and practitioners. Recently, data stream mining has emerged as an important subfield of data mining, because data samples usually are generated in a sequence over time and collected in a form of a stream in many cases in the real world. In this paper, we study a real-world problem and apply data stream mining techniques to the prediction of Taiwan Stock Exchange Capitalization Weighted Stock Index Futures (TAIEX Futures). We model the problem as a binary classification problem and our goal is to predict the rising or falling of the short-term futures. We design the data pre-processing procedure and employ a data stream miming toolkit in experiments. The results indicate that the concept drift detection method is helpful for TAIEX Futures in which concept drift supposedly exists and also that data stream mining technology is helpful for predicting the futures market. © 2012 IEEE.
dc.format.extent 176 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Proceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012
dc.relation (關聯) 10.1109/GrC.2012.6468567
dc.subject (關鍵詞) Binary classification problems; Data preprocessing; Data stream mining; Empirical studies; futures; Real-world problem; Stock index futures; Taiwan stock exchanges; Classification (of information); Data communication systems; Forecasting; Granular computing; Data mining
dc.title (題名) An empirical study of applying data mining techniques to the prediction of TAIEX Futures
dc.type (資料類型) conferenceen
dc.identifier.doi (DOI) 10.1109/GrC.2012.6468567en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1109/GrC.2012.6468567en_US