  Subsequence anomaly detection in long sequences is an important problem with
applications in a wide range of domains. However, the approaches proposed so
far in the literature have severe limitations: they either require prior domain
knowledge used to design the anomaly discovery algorithms, or become cumbersome
and expensive to use in situations with recurrent anomalies of the same type.
In this work, we address these problems, and propose an unsupervised method
suitable for domain agnostic subsequence anomaly detection. Our method,
Series2Graph, is based on a graph representation of a novel low-dimensionality
embedding of subsequences. Series2Graph needs neither labeled instances (like
supervised techniques) nor anomaly-free data (like zero-positive learning
techniques), and identifies anomalies of varying lengths. 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 without any prior knowledge of their characteristics,
outperforming by a large margin several competing approaches in accuracy, while
being up to orders of magnitude faster. This paper has appeared in VLDB 2020.
