Outlier mining on multiple time series data in stock market

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

Outlier mining on multiple time series data in stock market

Luo, C Zhao, Y

Cao, L

Ou, Y Liu, L

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, 5351 LNAI pp. 1010 - 1015
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Luo, C	en_US
dc.contributor.author	Zhao, Y https://orcid.org/0000-0002-0209-3971	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	Liu, L	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, 5351 LNAI pp. 1010 - 1015	en_US
dc.identifier.isbn	354089196X	en_US
dc.identifier.isbn	9783540891963	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10901
dc.description.abstract	With the dramatic increase of stock market data, traditional outlier mining technologies have shown their limitations in efficiency and precision. In this paper, an outlier mining model on stock market data is proposed, which aims to detect the anomalies from multiple complex stock market data. This model is able to improve the precision of outlier mining on individual time series. The experiments on real-world stock market data show that the proposed outlier mining model is effective and outperforms traditional technologies. © 2008 Springer Berlin Heidelberg.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-540-89197-0_99	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Outlier mining on multiple time series data in stock market	en_US
dc.type	Conference Proceeding
utslib.citation.volume	5351 LNAI	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Hanoi, Vietnam	en_US
pubs.embargo.period	Not known	en_US
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 - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	5351 LNAI	en_US

Abstract:

With the dramatic increase of stock market data, traditional outlier mining technologies have shown their limitations in efficiency and precision. In this paper, an outlier mining model on stock market data is proposed, which aims to detect the anomalies from multiple complex stock market data. This model is able to improve the precision of outlier mining on individual time series. The experiments on real-world stock market data show that the proposed outlier mining model is effective and outperforms traditional technologies. © 2008 Springer Berlin Heidelberg.

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http://hdl.handle.net/10453/10901