4 citations · 4 across the 9 of their papers we have counts for
6 papers · 1 filter
Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System
Zhiwen Yu, Derong Yang, Liujian Zhang +5
Partial differential equations (PDEs) play a central role in modeling complex physical, biological, and engineering systems. While traditional numerical solvers are robust, they of…
Labels Matter More Than Models: Rethinking the Unsupervised Paradigm in Time Series Anomaly Detection
Zhijie Zhong, Zhiwen Yu, Kaixiang Yang +3
Time series anomaly detection (TSAD) is a critical data mining task often constrained by label scarcity. Consequently, current research predominantly focuses on Unsupervised Time-s…
ScatterAD: Temporal-Topological Scattering Mechanism for Time Series Anomaly Detection
Tao Yin, Xiaohong Zhang, Shaochen Fu +5
One main challenge in time series anomaly detection for industrial IoT lies in the complex spatio-temporal couplings within multivariate data. However, traditional anomaly detectio…
CCE: Confidence-Consistency Evaluation for Time Series Anomaly Detection
Zhijie Zhong, Zhiwen Yu, Yiu-ming Cheung +1
Time Series Anomaly Detection metrics serve as crucial tools for model evaluation. However, existing metrics suffer from several limitations: insufficient discriminative power, str…
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…
SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detection
Zhijie Zhong, Zhiwen Yu, Xing Xi +5
Despite the prevalence of reconstruction-based deep learning methods, time series anomaly detection remains a tremendous challenge. Existing approaches often struggle with limited…