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Multiple time series anomaly detection based on compression and correlation analysis: a medical surveillance case study

Qiao, Zhi, He, Jing, Cao, Jie, Huang, Guangyan and Zhang, Peng 2012, Multiple time series anomaly detection based on compression and correlation analysis: a medical surveillance case study, in 14th Asia-Pacific Web Conference, APWeb 2012, Kunming, China, April 11-13, 2012. Proceedings, Springer, Berlin, Germany, pp. 294-305, doi: 10.1007/978-3-642-29253-8_25.

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Title Multiple time series anomaly detection based on compression and correlation analysis: a medical surveillance case study
Author(s) Qiao, Zhi
He, Jing
Cao, Jie
Huang, Guangyan
Zhang, Peng
Conference name Asia Pacific Web Technology Conference (14th : 2012 : Kunming, China)
Conference location Kunming, China
Conference dates 11-13 Apr. 2012
Title of proceedings 14th Asia-Pacific Web Conference, APWeb 2012, Kunming, China, April 11-13, 2012. Proceedings
Publication date 2012
Series Lecture Notes in Computer Science v.7235
Conference series Web Technologies and Applications
Start page 294
End page 305
Total pages 12
Publisher Springer
Place of publication Berlin, Germany
Summary In this paper, we present a novel anomaly detection framework for multiple heterogeneous yet correlated time series, such as the medical surveillance series data. In our framework, we propose an anomaly detection algorithm from the viewpoint of trend and correlation analysis. Moreover, to efficiently process huge amount of observed time series, a new clustering-based compression method is proposed. Experimental results indicate that our framework is more effective and efficient than its peers. © 2012 Springer-Verlag Berlin Heidelberg.
ISBN 9783642292521
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-642-29253-8_25
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2012, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083689

Document type: Conference Paper
Collection: School of Information Technology
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