Title: Anomalistic sequence detection

Authors: Mingyan Teng; Ying-Yuan Xiao

Addresses: Department of Mathematics, Bohai University, Jinzhou, Liaoning 121000, China. ' Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Key Laboratory of Computer Vision and System, Tianjin University of Technology, Tianjin 300191, China

Abstract: Given a set of training instances of sequences (time series), the problem of anomalistic sequence detection is to predict whether a newly observed time series novel or normal. Anomalistic sequence detection is very useful in many monitoring applications such as video surveillance and signal recognition. In this paper, we extend existing distance-based outlier detection algorithms to address the anomaly detection problem, and propose an instance-based anomaly detection algorithm. We study the effectiveness of these algorithms on some commonly used distance measures of time series. Experiments show that the instance-based algorithm under warping distances such as DTW and EDR achieves much better accuracy than the other combination. We observe that the actual distance calculation of warping distances contributes the main bulk of computational cost of anomaly detection. To improve the efficiency, we propose a local instance summarisation approach, called VarSpace, which reduces distance calculation by summarising similar training instances. Experiments show that the VarSpace approach can improve the efficiency of instance-based anomaly detection significantly.

Keywords: anomaly detection; outlier detection; sequence detection; time series; monitoring; video surveillance; signal recognition.

DOI: 10.1504/IJIIDS.2012.047001

International Journal of Intelligent Information and Database Systems, 2012 Vol.6 No.3, pp.201 - 219

Received: 14 May 2010
Accepted: 09 Mar 2011
Published online: 16 Aug 2014 *

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