[CIKM 2023] This is the official source code of "TrendGCN: Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting" based on Pytorch.
@RongzhangCheng Hi, thanks for your interest. : )
If you want to cite our paper, please add the following BibTeX information to your xx.bib file of your LaTex source code.
@article{jiang2022dynamic,
title={Dynamic Adaptive and Adversarial Graph Convolutional Network for Traffic Forecasting},
author={Jiang, Juyong and Wu, Binqing and Chen, Ling and Kim, Sunghun},
journal={arXiv preprint arXiv:2208.03063},
year={2022}
}
Sorry for the inconvenience, we have changed the original title of the paper due to substantial revision of the content. The original title of the paper is "Dynamic Adaptive and Adversarial Graph Convolutional Network for Traffic Forecasting". If you don't mind, you can cite this version since the proceeding information of CIKM 2023 is not available yet.
@RongzhangCheng Hi, thanks for your interest. : ) If you want to cite our paper, please add the following BibTeX information to your
xx.bibfile of your LaTex source code.Sorry for the inconvenience, we have changed the original title of the paper due to substantial revision of the content. The original title of the paper is "Dynamic Adaptive and Adversarial Graph Convolutional Network for Traffic Forecasting". If you don't mind, you can cite this version since the proceeding information of CIKM 2023 is not available yet.