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

Authors:
Zhi Qiao;Jing He;Jie Cao;Guangyan Huang;Peng Zhang
Affiliations:
Victoria University, Melbourne, Australia and Institute of Information Engineering, Chinese Academic of Science, China;Victoria University, Melbourne, Australia;Nanjing University of Finance and Economics, Nanjing, China;Victoria University, Melbourne, Australia;Institute of Information Engineering, Chinese Academic of Science, China
Venue:
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Year:
2012

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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.