![]() ![]() ![]() Series2Graph needs neither labeled instances (like supervised techniques), nor anomaly-free data (like zero-positive learning techniques), and identifies anomalies of varying lengths. Our method, Series2Graph, is based on a graph representation of a novel low-dimensionality embedding of subsequences. In this work, we address these problems, and propose an unsupervised method suitable for domain agnostic subsequence anomaly detection. However, the approaches that have been proposed so far in the literature have severe limitations: they either require prior domain knowledge that is used to design the anomaly discovery algorithms, or become cumbersome and expensive to use in situations with recurrent anomalies of the same type. Subsequence anomaly detection in long sequences is an important problem with applications in a wide range of domains.
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