arXiv:2509. 06419v2 Announce Type: replace Abstract: Time-series anomaly detection is crucial in AIOps for maintaining large-scale service reliability.
arXiv:2608. 09246v1 Announce Type: new Abstract: Industrial processes are complex systems composed of multiple interacting sensors that generate multivariate time series (MTS).
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.
arXiv:2604. 13924v3 Announce Type: replace-cross Abstract: Time-series anomaly detection (TSAD) is critical in domains such as industrial monitoring, healthcare, and cybersecurity, but it remains challenging due to rare and heterogeneous anomalies and the scarcity of labelled data.
arXiv:2604. 14221v2 Announce Type: replace Abstract: Reliable evaluation of anomaly detection methods in multivariate time series remains an open challenge, largely due to the limitations of existing benchmark datasets.
arXiv:2606. 05274v1 Announce Type: new Abstract: Electro-Hydrostatic Actuators (EHAs) are widely used in aerospace and industrial systems, where timely detection of sensor anomalies is essential to ensure safe and reliable operation.
arXiv:2607. 02046v1 Announce Type: new Abstract: Anomaly detection is a critical and evolving field in Machine Learning, with applications targeting different domains such as cybersecurity, finance, healthcare, manufacturing and IoT (Internet of Things) systems.
arXiv:2608. 10587v1 Announce Type: new Abstract: Artificial Intelligence (AI)-based prospective anomaly detection methods are increasingly deployed in high-dimensional and nonlinear settings.
arXiv:2607. 00720v1 Announce Type: cross Abstract: Despite the increasing sophistication of industrial AI systems, the ability to reliably detect subtle and noisy anomalies in complex time series data remains a critical yet unresolved challenge.
Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition introduces C-PP-COAD, a framework that uses synthetic calibration data to reduce reliance on real-world calibration while maintaining assumption-free false discovery rate control. The method wraps any anomaly detection algorithm, converting its scores into conformal p-values for online testing. Experiments on synthetic and real datasets—including thyroid dysfunction, O‑RAN conflict, 5G intrusion, and UE throughput degradation—show that C-PP-COAD preserves FDR guarantees while significantly cutting the need for real calibration data.
arXiv:2607. 18289v1 Announce Type: cross Abstract: Continual anomaly detection (CAD) studies how models can adapt to evolving data distributions while retaining performance on previously observed regimes.
Anomaly detection is a critical and evolving field in Machine Learning, with applications targeting different domains such as cybersecurity, finance, healthcare, manufacturing and IoT (Internet of Things) systems. Traditionally, anomaly detection algorithms have been designed using both supervised and unsupervised learning paradigms.