3 papers
cs.LG2026
Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretability
Roberto Stanzione, Jules Barbe, Magali Parrino +2
Time Series Anomaly Detection has received increasing attention, driven by the growing availability of complex time series data. This surge has led to the development of numerous d…
cs.LG2026
What Streaming Anomaly Detection Finds (and Misses) in Industrial Time Series
Magali Parrino, Antoine Ajenjo, Emmanuel Remy +2
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 setti…
cs.LG2026
In a Streaming World, Should You Stand Still? A Comprehensive Benchmark of Anomaly Detection in Streams
Magali Parrino, Antoine Ajenjo, Emmanuel Remy +3
Time series anomaly detection (TSAD) is increasingly deployed in streaming settings, where data arrive sequentially and may exhibit non-stationarity. As a result, several works fro…