9 citations · 18 across the 13 of their papers we have counts for
5 papers · 1 filter
Prospective Multi-Graph Cohesion for Multivariate Time Series Anomaly Detection
Jiazhen Chen, Mingbin Feng, Tony S. Wirjanto
Anomaly detection in high-dimensional time series data is pivotal for numerous industrial applications. Recent advances in multivariate time series anomaly detection (TSAD) have in…
Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs
Jiazhen Chen, Sichao Fu, Zheng Ma +3
Semi-supervised graph anomaly detection (GAD) has recently received increasing attention, which aims to distinguish anomalous patterns from graphs under the guidance of a moderate…
Towards Cross-domain Few-shot Graph Anomaly Detection
Jiazhen Chen, Sichao Fu, Zhibin Zhang +4
Few-shot graph anomaly detection (GAD) has recently garnered increasing attention, which aims to discern anomalous patterns among abundant unlabeled test nodes under the guidance o…
Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection
Katrina Chen, Mingbin Feng, Tony S. Wirjanto
Time series anomaly detection (TSAD) plays a vital role in many industrial applications. While contrastive learning has gained momentum in the time series domain for its prowess in…
Multivariate Time Series Anomaly Detection via Dynamic Graph Forecasting
Katrina Chen, Mingbin Feng, Tony S. Wirjanto
Anomalies in univariate time series often refer to abnormal values and deviations from the temporal patterns from majority of historical observations. In multivariate time series,…