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.
CITATION STYLE
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7235 LNCS, pp. 294–305). https://doi.org/10.1007/978-3-642-29253-8_25
Mendeley helps you to discover research relevant for your work.