298 citations · 333 across the 10 of their papers we have counts for
8 papers · 1 filter
STA-GANN: A Valid and Generalizable Spatio-Temporal Kriging Approach
Yujie Li, Zezhi Shao, Chengqing Yu +6
Spatio-temporal tasks often encounter incomplete data arising from missing or inaccessible sensors, making spatio-temporal kriging crucial for inferring the completely missing temp…
GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing
Chengqing Yu, Fei Wang, Zezhi Shao +4
Multivariate time series forecasting (MTSF) is crucial for decision-making to precisely forecast the future values/trends, based on the complex relationships identified from histor…
Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis
Zezhi Shao, Fei Wang, Yongjun Xu +10
Multivariate Time Series (MTS) analysis is crucial to understanding and managing complex systems, such as traffic and energy systems, and a variety of approaches to MTS forecasting…
HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting
Zezhi Shao, Fei Wang, Tao Sun +7
Traffic forecasting, which aims to predict traffic conditions based on historical observations, has been an enduring research topic and is widely recognized as an essential compone…
Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting
Zezhi Shao, Zhao Zhang, Fei Wang +2
Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly po…
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting
Zezhi Shao, Zhao Zhang, Fei Wang +1
Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly po…
Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting
Zezhi Shao, Zhao Zhang, Wei Wei +4
We all depend on mobility, and vehicular transportation affects the daily lives of most of us. Thus, the ability to forecast the state of traffic in a road network is an important…
Heterogeneous Graph Neural Network with Multi-view Representation Learning
Zezhi Shao, Yongjun Xu, Wei Wei +3
Graph neural networks for heterogeneous graph embedding is to project nodes into a low-dimensional space by exploring the heterogeneity and semantics of the heterogeneous graph. Ho…