Research
What Streaming Anomaly Detection Finds (and Misses) in Industrial Time Series
arXiv:2609.39232v1 Announce Type: cross 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 streamin
arXiv:2609.39232v1 Announce Type: cross 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.
Related
- Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate
- ECoLAD: Deployment-Oriented Evaluation for Automotive Time-Series Anomaly Detection
- TPA-AD: A Two-Stage Pseudo Anomaly-Guided Method for Bearing Time-Series Anomaly Detection
- Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy
Source: arXiv cs.AI | 2026-10-01