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StemGNN should be included in PyPOTS, and could start from the imputation task.
@inproceedings{cao2020stemgnn,
author = {Cao, Defu and Wang, Yujing and Duan, Juanyong and Zhang, Ce and Zhu, Xia and Huang, Congrui and Tong, Yunhai and Xu, Bixiong and Bai, Jing and Tong, Jie and Zhang, Qi},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {17766--17778},
publisher = {Curran Associates, Inc.},
title = {Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/cdf6581cb7aca4b7e19ef136c6e601a5-Paper.pdf},
volume = {33},
year = {2020}
}
2. Check open-source status
The model implementation is publicly available
3. Provide useful information for the implementation
1. Model description
StemGNN should be included in PyPOTS, and could start from the imputation task.
2. Check open-source status
3. Provide useful information for the implementation
https://github.com/microsoft/StemGNN
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