Exception mining on multiple time series in stock market

Luo, C; Zhao, Y; Cao, L; Ou, Y; Zhang, C

Exception mining on multiple time series in stock market

Luo, C Zhao, Y Cao, L

Ou, Y Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008, 2008, pp. 690 - 693
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Luo, C	en_US
dc.contributor.author	Zhao, Y	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Ou, Y	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008, 2008, pp. 690 - 693	en_US
dc.identifier.isbn	9780769534961	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10862
dc.description.abstract	This paper presents our research on exception mining on multiple time series data which aims to assist stock market surveillance by identifying market anomalies. Traditional technologies on stock market surveillance have shown their limitations to handle large amount of complicated stock market data. In our research, the Outlier Mining on Multiple time series (OMM) is proposed to improve the effectiveness of exception detection for stock market surveillance. The idea of our research is presented, challenges on the research are analyzed, and potential research directions are summarized. © 2008 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0667060
dc.relation	http://purl.org/au-research/grants/arc/DP0773412
dc.relation	http://purl.org/au-research/grants/arc/LP0775041R1
dc.relation.ispartof	Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008	en_US
dc.relation.isbasedon	10.1109/WIIAT.2008.302	en_US
dc.title	Exception mining on multiple time series in stock market	en_US
dc.type	Conference Proceeding
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080110 Simulation and Modelling	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Sydney, Australia	en_US
dc.location.activity	Rhodes, Greece
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper presents our research on exception mining on multiple time series data which aims to assist stock market surveillance by identifying market anomalies. Traditional technologies on stock market surveillance have shown their limitations to handle large amount of complicated stock market data. In our research, the Outlier Mining on Multiple time series (OMM) is proposed to improve the effectiveness of exception detection for stock market surveillance. The idea of our research is presented, challenges on the research are analyzed, and potential research directions are summarized. © 2008 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/10862