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.
This work is supported by ARC Linkage project LP100200682: Real-time and Self- Adaptive Stream Data Analyzer for Intensive Care Management and the National Science Foundation of China (NSFC) under Grant No. 71072172.
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Qiao, Z., He, J., Cao, J., Huang, G., Zhang, P. (2012). Multiple Time Series Anomaly Detection Based on Compression and Correlation Analysis: A Medical Surveillance Case Study. In: Sheng, Q.Z., Wang, G., Jensen, C.S., Xu, G. (eds) Web Technologies and Applications. APWeb 2012. Lecture Notes in Computer Science, vol 7235. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29253-8_25
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DOI: https://doi.org/10.1007/978-3-642-29253-8_25
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