no code implementations • 19 Dec 2023 • Yujie Li, Zezhi Shao, Yongjun Xu, Qiang Qiu, Zhaogang Cao, Fei Wang
Complex spatial dependencies in transportation networks make traffic prediction extremely challenging.
3 code implementations • 9 Oct 2023 • Zezhi Shao, Fei Wang, Yongjun Xu, Wei Wei, Chengqing Yu, Zhao Zhang, Di Yao, Guangyin Jin, Xin Cao, Gao Cong, Christian S. Jensen, Xueqi Cheng
Moreover, based on the proposed BasicTS and rich heterogeneous MTS datasets, we conduct an exhaustive and reproducible performance and efficiency comparison of popular models, providing insights for researchers in selecting and designing MTS forecasting models.
no code implementations • 7 Aug 2023 • Chengqing Yu, Fei Wang, Zezhi Shao, Tao Sun, Lin Wu, Yongjun Xu
Multivariate time series long-term prediction, which aims to predict the change of data in a long time, can provide references for decision-making.
no code implementations • 27 Jul 2023 • Zezhi Shao, Fei Wang, Zhao Zhang, Yuchen Fang, Guangyin Jin, Yongjun Xu
Then, we propose a novel Hierarchical U-net TransFormer (HUTFormer) to address the issues of long-term traffic forecasting.
no code implementations • 25 Mar 2023 • Guangyin Jin, Yuxuan Liang, Yuchen Fang, Zezhi Shao, Jincai Huang, Junbo Zhang, Yu Zheng
STGNNs enable the extraction of complex spatio-temporal dependencies by integrating graph neural networks (GNNs) and various temporal learning methods.
1 code implementation • 10 Aug 2022 • Zezhi Shao, Zhao Zhang, Fei Wang, Wei Wei, Yongjun Xu
These results suggest that we can design efficient and effective models as long as they solve the indistinguishability of samples, without being limited to STGNNs.
2 code implementations • 18 Jun 2022 • Zezhi Shao, Zhao Zhang, Fei Wang, Yongjun Xu
However, the patterns of time series and the dependencies between them (i. e., the temporal and spatial patterns) need to be analyzed based on long-term historical MTS data.
Ranked #2 on Traffic Prediction on PEMS-BAY (using extra training data)
1 code implementation • 18 Jun 2022 • Zezhi Shao, Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, Christian S. Jensen
However, intuitively, traffic data encompasses two different kinds of hidden time series signals, namely the diffusion signals and inherent signals.
Ranked #3 on Traffic Prediction on PEMS-BAY
no code implementations • 31 Aug 2021 • Zezhi Shao, Yongjun Xu, Wei Wei, Fei Wang, Zhao Zhang, Feida Zhu
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.