298 citations · 429 across the 17 of their papers we have counts for
10 papers · 1 filter
On the Integration of Spatial-Temporal Knowledge: A Lightweight Approach to Atmospheric Time Series Forecasting
Yisong Fu, Fei Wang, Zezhi Shao +6
Transformers have gained attention in atmospheric time series forecasting (ATSF) for their ability to capture global spatial-temporal correlations. However, their complex architect…
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…
Dynamic Frequency Domain Graph Convolutional Network for Traffic Forecasting
Yujie Li, Zezhi Shao, Yongjun Xu +3
Complex spatial dependencies in transportation networks make traffic prediction extremely challenging. Much existing work is devoted to learning dynamic graph structures among sens…
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…
DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Prediction
Chengqing Yu, Fei Wang, Zezhi Shao +3
Multivariate time series long-term prediction, which aims to predict the change of data in a long time, can provide references for decision-making. Although transformer-based model…
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…