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

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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: Web Technologies and Applications: 14th Asia-Pacific Web Conference, APWeb 2012, Kunming, China, April 11-13, 2012. Proceedings. Sheng, Quan Z, Wang, Guoren, Jensen, Christian S and Xu, Guandong, eds. Lecture Notes in Computer Science (7235). Springer, Heidelberg, Germany, pp. 294-305.

Abstract

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.

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Item type Book Section
URI https://vuir.vu.edu.au/id/eprint/22125
DOI https://doi.org/10.1007/978-3-642-29253-8_25
Official URL http://link.springer.com/content/pdf/10.1007%2F978...
ISBN 9783642292521 (print) 9783642292538 (online)
Funders http://purl.org/au-research/grants/arc/LP100200682
Subjects Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing
Historical > FOR Classification > 0806 Information Systems
Historical > Faculty/School/Research Centre/Department > Centre for Applied Informatics
Current > Division/Research > College of Science and Engineering
Keywords ResPubID25935, data mining, knowledge discovery, pattern recognition, database management, computer communication networks
Citations in Scopus 5 - View on Scopus
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