22 citations · 47 across the 10 of their papers we have counts for
4 papers · 1 filter
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning
Junfeng Hu, Xu Liu, Zhencheng Fan +2
Spatio-temporal graph learning is a fundamental problem in modern urban systems. Existing approaches tackle different tasks independently, tailoring their models to unique task cha…
UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting
Xu Liu, Junfeng Hu, Yuan Li +4
Multivariate time series forecasting plays a pivotal role in contemporary web technologies. In contrast to conventional methods that involve creating dedicated models for specific…
LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting
Xu Liu, Yutong Xia, Yuxuan Liang +7
Road traffic forecasting plays a critical role in smart city initiatives and has experienced significant advancements thanks to the power of deep learning in capturing non-linear p…
Do We Really Need Graph Neural Networks for Traffic Forecasting?
Xu Liu, Yuxuan Liang, Chao Huang +4
Spatio-temporal graph neural networks (STGNN) have become the most popular solution to traffic forecasting. While successful, they rely on the message passing scheme of GNNs to est…