13 papers
Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection
Yingjie Zhou, Yuqin Xie, Fanxing Liu +3
Weakly supervised graph anomaly detection aims to unveil unusual graph instances, e.g., nodes, whose behaviors significantly differ from normal ones, given only a limited number of…
Uncertainty-Guided Latent Diagnostic Trajectory Learning for Sequential Clinical Diagnosis
Xuyang Shen, Haoran Liu, Dongjin Song +1
Clinical diagnosis requires sequential evidence acquisition under uncertainty. However, most Large Language Model (LLM) based diagnostic systems assume fully observed patient infor…
Empowering Power Outage Prediction with Spatially Aware Hybrid Graph Neural Networks and Contrastive Learning
Xuyang Shen, Zijie Pan, Diego Cerrai +4
Extreme weather events, such as severe storms, hurricanes, snowstorms, and ice storms, which are exacerbated by climate change, frequently cause widespread power outages. These out…
TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop
Yushan Jiang, Wenchao Yu, Geon Lee +5
Time series analysis provides essential insights for real-world system dynamics and informs downstream decision-making, yet most existing methods often overlook the rich contextual…
FTSCommDetector: Discovering Behavioral Communities through Temporal Synchronization
Tianyang Luo, Xikun Zhang, Dongjin Song
Why do trillion-dollar tech giants AAPL and MSFT diverge into different response patterns during market disruptions despite identical sector classifications? This paradox reveals a…
TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster
Kanghui Ning, Zijie Pan, Yu Liu +7
Large Language Models (LLMs) and Foundation Models (FMs) have recently become prevalent for time series forecasting tasks. While fine-tuning LLMs enables domain adaptation, they of…