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arXiv · 2609.39232

What Streaming Anomaly Detection Finds (and Misses) in Industrial Time Series

Magali Parrino·Antoine Ajenjo·Emmanuel Remy·Pierre Stephan·Paul Boniol
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Abstract

EDF relies on continuous monitoring of its power plants to detect anomalies as soon as they occur. Given the absence of a universally optimal streaming method in unsupervised settings, we compare streaming methods with state-of-the-art TSAD models deployed online on a real nuclear power plant dataset. This work also evaluates Automated Anomaly Detection in a streaming context. Results show higher consistency for online TSAD and strong robustness from ensembling strategies.

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Magali Parrino, Antoine Ajenjo, Emmanuel Remy, Pierre Stephan, Paul Boniol. 2026-09-30. What Streaming Anomaly Detection Finds (and Misses) in Industrial Time Series. https://arxiv.org/abs/2609.39232

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