SAD: An Unsupervised System for Subsequence Anomaly Detection
Subsequence anomaly (or outlier) detection in long sequences is an important problem with applications in a wide range of domains. However, current approaches have severe limitations: they either require prior domain knowledge, or become cumbersome and expensive to use in situations with recurrent anomalies of the same type. We recently proposed NorM, a novel approach suitable for domain agnostic anomaly detection, which addresses the aforementioned problems by detecting anomalies based on their (dis)similarity to a model that represents normal behavior. The experimental results on several real datasets demonstrate that the proposed approach outperforms the current state of the art in terms of both accuracy and execution time. In this demonstration, we present a system for unsupervised Subsequence Anomaly Detection (SAD) that uses the NorM method. Through various scenarios with real datasets, we showcase the challenges of the problem, and we demonstrate the advantages of the proposed system.
2020 IEEE 36th International Conference on Data Engineering (ICDE)