Efficient segmentation-based methods for anomaly detection in static and streaming time series under dynamic time warping

Abstract

The problem of time series anomaly detection has attracted a lot of attention due to its usefulness in various application domains. However, most of the methods proposed so far used Euclidean distance to deal with this problem. Dynamic Time Warping (DTW) distance is more suitable than Euclidean distance because of its capability in shape-based similarity checking in many practical fields, for example those with multimedia data. In this paper, we propose two efficient anomaly detection methods, EP-Leader-DTW and SEP-Leader-DTW, for static and streaming time series under DTW, respectively. Our methods are based on time series segmentation, subsequence clustering, and anomaly scoring. For segmentation, the major extrema method is used to obtain subsequences. For clustering, we apply Leader algorithm to cluster the subsequences along with a lower bounding technique to accelerate DTW distance computation. Experimental results on several benchmark time series datasets reveal that our method for anomaly detection in static time series under DTW can perform very fast and accurately on large time series datasets. For streaming time series, our method can meet the instantaneous requirement with high accuracy. As a result, our anomaly detection methods are applicable to both static and streaming time series in practice.