13 papers
Learning Spatio-Temporal Foundation Models from Pure Synthetic Data
Yutong Feng, Shiyuan Piao, Yutong Xia +5
Spatio-Temporal Foundation Models (STFMs) aim to learn generalizable representations of complex dynamical systems across space and time. However, existing approaches suffer from di…
Causal Time Series Generation via Diffusion Models
Yutong Xia, Chang Xu, Yuxuan Liang +4
Time series generation (TSG) synthesizes realistic sequences and has achieved remarkable success. Among TSG, conditional models generate sequences given observed covariates, howeve…
FlowNet: Modeling Dynamic Spatio-Temporal Systems via Flow Propagation
Yutong Feng, Xu Liu, Yutong Xia +1
Accurately modeling complex dynamic spatio-temporal systems requires capturing flow-mediated interdependencies and context-sensitive interaction dynamics. Existing methods, predomi…
CaPulse: Detecting Anomalies by Tuning in to the Causal Rhythms of Time Series
Yutong Xia, Yingying Zhang, Yuxuan Liang +3
Time series anomaly detection has garnered considerable attention across diverse domains. While existing methods often fail to capture the underlying mechanisms behind anomaly gene…
AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks
Qiongyan Wang, Yutong Xia, Siru ZHong +6
Monitoring real-time air quality is essential for safeguarding public health and fostering social progress. However, the widespread deployment of air quality monitoring stations is…
Reimagining Urban Science: Scaling Causal Inference with Large Language Models
Yutong Xia, Ao Qu, Yunhan Zheng +8
Urban causal research is essential for understanding the complex, dynamic processes that shape cities and for informing evidence-based policies. However, current practices are ofte…