3 papers
cs.LG2026
IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detection
Xiaohui Zhou, Yijie Wang, Hongzuo Xu +3
Open-set anomaly detection (OSAD) is an emerging paradigm designed to utilize limited labeled data from anomaly classes seen in training to identify both seen and unseen anomalies…
cs.LG2024
Angel or Devil: Discriminating Hard Samples and Anomaly Contaminations for Unsupervised Time Series Anomaly Detection
Ruyi Zhang, Hongzuo Xu, Songlei Jian +3
Training in unsupervised time series anomaly detection is constantly plagued by the discrimination between harmful `anomaly contaminations' and beneficial `hard normal samples'. Th…
cs.LG2024
Abnormality Forecasting: Time Series Anomaly Prediction via Future Context Modeling
Sinong Zhao, Wenrui Wang, Hongzuo Xu +5
Identifying anomalies from time series data plays an important role in various fields such as infrastructure security, intelligent operation and maintenance, and space exploration.…