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

arXiv: https://arxiv.org/abs/2609.39232 · HTML: https://mlsanity.com/paper/2609.39232/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 30 Sep 2026.

## Builds on (most cited first, 2 of 2)

- [Dive into Time-Series Anomaly Detection: A Decade Review](https://mlsanity.com/paper/2412.20512/) (2024)
- [VUS: Effective and Efficient Accuracy Measures for Time-Series Anomaly Detection](https://mlsanity.com/paper/2502.13318/) (2025)

## Abstract (arXiv)

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.