Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection

no code implementations • 24 May 2024 • Jun Liu, Chaoyun Zhang, Jiaxu Qian, Minghua Ma, Si Qin, Chetan Bansal, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang

Time series anomaly detection (TSAD) plays a crucial role in various industries by identifying atypical patterns that deviate from standard trends, thereby maintaining system integrity and enabling prompt response measures.

Anomaly Detection Decision Making +2

PathMLP: Smooth Path Towards High-order Homophily

no code implementations • 23 Jun 2023 • Chenxuan Xie, Jiajun Zhou, Shengbo Gong, Jiacheng Wan, Jiaxu Qian, Shanqing Yu, Qi Xuan, Xiaoniu Yang

However, common practices in GNNs to acquire high-order information mainly through increasing model depth and altering message-passing mechanisms, which, albeit effective to a certain extent, suffer from three shortcomings: 1) over-smoothing due to excessive model depth and propagation times; 2) high-order information is not fully utilized; 3) low computational efficiency.

Computational Efficiency Representation Learning