Paper Info
Title
An empirical study of concept drift detection for the prediction of TAIEX futures
Abstract
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.
Year
DOI
Venue
2013
10.1109/IWCIA.2013.6624804
IWCIA
Keywords
Field
DocType
commodity trading,data mining,financial data processing,learning (artificial intelligence),pattern classification,taiex futures prediction,taiwan stock exchange capitalization weighted stock index futures,concept drift detection,data distribution,data stream mining techniques,drift detection method,ensemble based learning method,financial market data,subclassifier,concept drift,data stream mining,futures,indexes,detectors,classification algorithms,accuracy,data models,niobium,learning artificial intelligence
Data mining,Data modeling,Data stream mining,Stock market index,Futures contract,Computer science,Stock exchange,Concept drift,Statistical classification,Empirical research
Conference
ISSN
ISBN
Citations 
1883-3977
978-1-4673-5725-8
0
PageRank 
References 
Authors
0.34
11
2
Name
Order
Citations
PageRank
Hong-Che Lin100.34
Kuo-Wei Hsu2536.38