1 code implementation • 18 May 2024 • Chengqing Yu, Fei Wang, Zezhi Shao, Tangwen Qian, Zhao Zhang, Wei Wei, Yongjun Xu
In GinAR, it consists of two key components, that is, interpolation attention and adaptive graph convolution to take place of the fully connected layer of simple recursive units, and thus are capable of recovering all missing variables and reconstructing the correct spatial-temporal dependencies for recursively modeling of multivariate time series data, respectively.
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.