2 papers
cs.LG2025
Rethinking the Unsupervised Paradigm in Time Series Anomaly Detection: An Empirical Study and Minimalist Baseline
Zhijie Zhong, Zhiwen Yu, Kaixiang Yang +3
Time series anomaly detection (TSAD) research predominantly focuses on unsupervised architectures to model normal data dynamics under label scarcity. However, practical deployments…
cs.LG2024
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System
Pengyu Li, Zhijie Zhong, Tong Zhang +3
Time series anomaly detection (TSAD) has been a research hotspot in both academia and industry in recent years. Deep learning methods have become the mainstream research direction…