NDLI: An empirical study of applying data mining techniques to the prediction of TAIEX Futures

Content Provider	IEEE Xplore Digital Library
Author	Hong-Che Lin Kuo-Wei Hsu
Copyright Year	2012
Description	Author affiliation: Department of Computer Science, National Chengchi University, Taipei, Taiwan (R.O.C.) (Hong-Che Lin; Kuo-Wei Hsu)
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.
Starting Page	277
Ending Page	282
File Size	898792
Page Count	6
File Format	PDF
ISBN	9781467323109
e-ISBN	9781467323116
DOI	10.1109/GrC.2012.6468567
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-11
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Adaptation models data stream mining Predictive models Data models Silicon Data mining futures classification
Content Type	Text
Resource Type	Article

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