GraphAn

Subsequence anomaly detection in long sequences is an important problem with applications in a wide range of domains. However, the state-of-the-art approaches have severe limitations: they either require prior domain knowledge, or become cumbersome and inefficient/ineffective in situations with recurrent anomalies of the same type. We recently proposed Series2Graph, a novel method based on a graph representation of a low-dimensionality embedding of subsequences, that detects anomalous subsequences. The experimental results, on the largest set of synthetic and real datasets used to date, demonstrate that the proposed approach correctly identifies single and recurrent anomalies of various types without any prior knowledge of the characteristics of these anomalies, outperforming by a large margin several competing approaches in accuracy, while being up to orders of magnitude faster. In this demonstration, we present GraphAn, a system based on Series2Graph, show-case the challenges of the problem, and demonstrate the advantages of the proposed system.