Anomalistic sequence detection

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.