What Streaming Anomaly Detection Finds (and Misses) in Industrial Time Series
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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arXiv:2609.39215v1 Announce Type: cross Abstract: Time series anomaly detection (TSAD) is increasingly deployed in streaming settings, where data arrive sequentially and may exhibit non-stationarity....
arXiv:2609.39257v1 Announce Type: cross Abstract: Accurate forecasting of electricity production is essential for maintaining the operational efficiency and strategic planning of energy utilities. In...
arXiv:2606. 18898v1 Announce Type: new Abstract: Multivariate time series anomaly detection (MTSAD) is critical for a wide range of application areas, such as industrial monitoring, cybersecurity, or healthcare.
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arXiv:2509. 06419v2 Announce Type: replace Abstract: Time-series anomaly detection is crucial in AIOps for maintaining large-scale service reliability.
arXiv:2609.13940v1 Announce Type: new Abstract: Effective anomaly detection frameworks are a central pillar of the Industry 4.0 paradigm. In this paper, we introduce an Optimal Transport (OT)-based f...