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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/74478

Title:	An empirical study of applying data mining techniques to the prediction of TAIEX Futures
Authors:	Lin, H.-C.;Hsu, Kuo-Wei 徐國偉
Contributors:	資科系
Keywords:	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
Date:	2012
Issue Date:	2015-04-10 16:38:26 (UTC+8)
Abstract:	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.
Relation:	Proceedings - 2012 IEEE International Conference on Granular Computing, GrC 2012 10.1109/GrC.2012.6468567
Data Type:	conference
DOI link:	http://dx.doi.org/10.1109/GrC.2012.6468567
DOI:	10.1109/GrC.2012.6468567
Appears in Collections:	[Department of Computer Science ] Proceedings

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