Making the End-User a Priority in Benchmarking: OrionBench for Unsupervised Time Series Anomaly Detection

arXiv:2310.17748v2 Announce Type: replace
Abstract: Time series anomaly detection is a prevalent problem in many application domains such as patient monitoring in healthcare, forecasting in finance, or predictive maintenance in energy. This has led to the emergence of a plethora of anomaly detection methods, including more recently, deep learning based methods. Although several benchmarks have been proposed to compare newly developed models, they usually rely on one-time execution over a limited set of datasets and the comparison is restricted to …

abstract anomaly anomaly detection application arxiv benchmarking cs.lg detection detection methods domains emergence energy finance forecasting healthcare maintenance making monitoring patient predictive predictive maintenance series the end time series type unsupervised