#6316. Unsupervised and scalable subsequence anomaly detection in large data series

August 2026publication date
Proposal available till 20-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Information Systems;
Hardware and Architecture;
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Abstract:
Subsequence anomaly (or outlier) detection in long sequences is an important problem with applications in a wide range of domains. However, the approaches that have been proposed so far in the literature 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. In this work, we address these problems and propose NormA, a novel approach, suitable for domain-agnostic anomaly detection. NormA is based on a new data series primitive, which permits to detect anomalies based on their (dis)similarity to a model that represents normal behavior.
Keywords:
Anomalies discovery; Data series; Time series

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