Outlier Mining on Multiple Time Series Data in Stock Market

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dc.contributor.author Luo, C
dc.contributor.author Zhao, Y
dc.contributor.author Cao, L
dc.contributor.author Ou, Y
dc.contributor.author Liu, L
dc.contributor.editor Ho, TB
dc.contributor.editor Zhou, ZH
dc.date.accessioned 2010-05-28T10:00:19Z
dc.date.issued 2008-01
dc.identifier.citation PRICAI 2008: Trends in Artificial Intelligence, 2008, pp. 1010 - 1015
dc.identifier.isbn 978-3-540-89196-3
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/10901
dc.description.abstract In stock market, the key surveillance function is identifying market anomalies, such as insider trading and market manipulation, to provide a fair and efficient trading platform [2,6]. Insider trading refers to the trades on privileged information unavailable to the public [8]. Market manipulation refers to the trade or action which aims to interfere with the demand or supply of a given stock to make the price increase or decrease in a particular way [3]. Recently, new intelligent technologies are required to deal with the challenges of the rapid increase of stock data. Outlier mining technologies have been used to detect market manipulation and insider trading . The objective of outlier mining is to find the data objects which are grossly different from or inconsistent with the majority of data. However, in stock market data, outliers are highly intermixed with normal data [4] and it is difficult to judge whether an object is an outlier or not. Therefore, a more effective and more efficient approach is in demand. This paper presents a new technique for outlier detection on multiple time series data in stock market. At first, principal curve algorithm is used to detect the outliers from individual measurements of stock market. Then, the generated outliers are measured with the probability of being real alerts. To improve the accuracy and precision, these outliers are combined by some rules associated with the domain knowledge. The experimental results on real stock market data show that the proposed model is feasible in practice and achieves a higher accuracy and precision than traditional methods
dc.publisher Springer
dc.relation.isbasedon 10.1007/978-3-540-89197-0_99
dc.title Outlier Mining on Multiple Time Series Data in Stock Market
dc.type Conference Proceeding
dc.parent PRICAI 2008: Trends in Artificial Intelligence
dc.journal.number en_US
dc.publocation Berlin en_US
dc.identifier.startpage 1010 en_US
dc.identifier.endpage 1015 en_US
dc.cauo.name FEIT.School of Software en_US
dc.conference Verified OK en_US
dc.conference Pacific Rim International Conference on Artificial Intelligence
dc.for 080109 Pattern Recognition and Data Mining
dc.personcode 034535
dc.personcode 021010
dc.personcode 999551
dc.personcode 998488
dc.personcode 100788
dc.percentage 100 en_US
dc.classification.name Pattern Recognition and Data Mining en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom Pacific Rim International Conference on Artificial Intelligence en_US
dc.date.activity 20080326 en_US
dc.date.activity 2008-03-26
dc.location.activity Hanoi, Vietnam en_US
dc.description.keywords Surveillance, privileged information, Outlier mining technologies, market manipulation and insider trading. en_US
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)
utslib.collection.history Uncategorised (ID: 363)


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