Discovering Semantics from Multiple Correlated Time Series Stream

Qiao, Z; Huang, G; He, J; Zhang, P; Guo, L; Cao, J; Zhang, Y

Discovering Semantics from Multiple Correlated Time Series Stream

Qiao, Z Huang, G He, J Zhang, P

Guo, L Cao, J Zhang, Y

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Publisher:: Elsevierer
Publication Type:: Journal Article
Citation:: Lecture Notes in Computer Science, 2013, 7819 pp. 509 - 520
Issue Date:: 2013-01

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Field	Value	Language
dc.contributor.author	Qiao, Z	en_US
dc.contributor.author	Huang, G	en_US
dc.contributor.author	He, J	en_US
dc.contributor.author	Zhang, P https://orcid.org/0000-0001-7973-2746	en_US
dc.contributor.author	Guo, L	en_US
dc.contributor.author	Cao, J	en_US
dc.contributor.author	Zhang, Y	en_US
dc.date.issued	2013-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science, 2013, 7819 pp. 509 - 520	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28910
dc.description.abstract	In this paper, we study a challenging problem of mining data generating rules and state transforming rules (i.e., semantics) underneath multiple correlated time series streams. A novel Correlation field-based Semantics Learning Framework (CfSLF) is proposed to learn the semantic. In the framework, we use Hidden Markov Random Field (HMRF) method to model relationship between latent states and observations in multiple correlated time series to learn data generating rules. The transforming rules are learned from corresponding latent state sequence of multiple time series based on Markov chain character. The reusable semantics learned by CfSLF can be fed into various analysis tools, such as prediction or anomaly detection. Moreover, we present two algorithms based on the semantics, which can later be applied to next-n step prediction and anomaly detection. Experiments on real world data sets demonstrate the efficiency and effectiveness of the proposed method.	en_US
dc.publisher	Elsevierer	en_US
dc.relation.ispartof	Lecture Notes in Computer Science	en_US
dc.relation.isbasedon	10.1007/978-3-642-37456-2_43	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Discovering Semantics from Multiple Correlated Time Series Stream	en_US
dc.type	Journal Article
utslib.citation.volume	7819	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.volume	7819	en_US

Abstract:

In this paper, we study a challenging problem of mining data generating rules and state transforming rules (i.e., semantics) underneath multiple correlated time series streams. A novel Correlation field-based Semantics Learning Framework (CfSLF) is proposed to learn the semantic. In the framework, we use Hidden Markov Random Field (HMRF) method to model relationship between latent states and observations in multiple correlated time series to learn data generating rules. The transforming rules are learned from corresponding latent state sequence of multiple time series based on Markov chain character. The reusable semantics learned by CfSLF can be fed into various analysis tools, such as prediction or anomaly detection. Moreover, we present two algorithms based on the semantics, which can later be applied to next-n step prediction and anomaly detection. Experiments on real world data sets demonstrate the efficiency and effectiveness of the proposed method.

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http://hdl.handle.net/10453/28910