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題名	An empirical study of concept drift detection for the prediction of TAIEX futures
作者	Lin, Hong-che;Hsu, Kuo-wei 徐國偉
貢獻者	資科系
關鍵詞	Concept drifts; Data stream mining; Detection methods; Ensemble-based method; Futures; Sequential manners; Stock index futures; Taiwan stock exchanges;Data communication systems; Finance; Artificial intelligence Concept drifts; Data stream mining; Detection methods; Ensemble-based method; Futures; Sequential manners; Stock index futures; Taiwan stock exchanges; Data communication systems; Finance; Artificial intelligence
日期	2013
上傳時間	16-Apr-2015 17:30:29 (UTC+8)
摘要	Financial market data is intrinsically dynamic, because it is usually generated in a sequential manner. Such dynamics are usually associated with concept drift, which indicates changes in the underlying data distribution. In this paper, we present our current work that extends from our previous work where we applied data stream mining techniques to the prediction of Taiwan Stock Exchange Capitalization Weighted Stock Index Futures, or TAIEX Futures. In order to analyze the type of concept drift existing in the TAIEX Futures data, we study various methods and propose an ensemble based method. The proposed method uses Drift Detection Method to determine the number of instances given to a sub-classifier that is a component of an ensemble and corresponds to a concept. By observing changes of relative weights of sub-classifiers, we can determine whether a concept occurs repeatedly. Moreover, compared to another ensemble based method, the proposed method achieves higher accuracy without knowing a parameter that is important for another method. © 2013 IEEE.
關聯	2013 IEEE 6th International Workshop on Computational Intelligence and Applications, IWCIA 2013 - Proceedings; Hiroshima; Japan; 13 July 2013 到 13 July 2013; 類別編號CFP1361U-ART;論文編號 6624804, Pages 155-160 10.1109/IWCIA.2013.6624804
資料類型	conference

dc.contributor	資科系
dc.creator (作者)	Lin, Hong-che;Hsu, Kuo-wei
dc.creator (作者)	徐國偉	zh_TW
dc.date (日期)	2013
dc.date.accessioned	16-Apr-2015 17:30:29 (UTC+8)	-
dc.date.available	16-Apr-2015 17:30:29 (UTC+8)	-
dc.date.issued (上傳時間)	16-Apr-2015 17:30:29 (UTC+8)	-
dc.identifier.uri (URI)	http://nccur.lib.nccu.edu.tw/handle/140.119/74628	-
dc.description.abstract (摘要)	Financial market data is intrinsically dynamic, because it is usually generated in a sequential manner. Such dynamics are usually associated with concept drift, which indicates changes in the underlying data distribution. In this paper, we present our current work that extends from our previous work where we applied data stream mining techniques to the prediction of Taiwan Stock Exchange Capitalization Weighted Stock Index Futures, or TAIEX Futures. In order to analyze the type of concept drift existing in the TAIEX Futures data, we study various methods and propose an ensemble based method. The proposed method uses Drift Detection Method to determine the number of instances given to a sub-classifier that is a component of an ensemble and corresponds to a concept. By observing changes of relative weights of sub-classifiers, we can determine whether a concept occurs repeatedly. Moreover, compared to another ensemble based method, the proposed method achieves higher accuracy without knowing a parameter that is important for another method. © 2013 IEEE.
dc.format.extent	176 bytes	-
dc.format.mimetype	text/html	-
dc.relation (關聯)	2013 IEEE 6th International Workshop on Computational Intelligence and Applications, IWCIA 2013 - Proceedings; Hiroshima; Japan; 13 July 2013 到 13 July 2013; 類別編號CFP1361U-ART;論文編號 6624804, Pages 155-160
dc.relation (關聯)	10.1109/IWCIA.2013.6624804
dc.subject (關鍵詞)	Concept drifts; Data stream mining; Detection methods; Ensemble-based method; Futures; Sequential manners; Stock index futures; Taiwan stock exchanges;Data communication systems; Finance; Artificial intelligence
dc.subject (關鍵詞)	Concept drifts; Data stream mining; Detection methods; Ensemble-based method; Futures; Sequential manners; Stock index futures; Taiwan stock exchanges; Data communication systems; Finance; Artificial intelligence
dc.title (題名)	An empirical study of concept drift detection for the prediction of TAIEX futures
dc.type (資料類型)	conference	en