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The PyTorch implementation of STGCN.
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The PyTorch implementation of STGCN from the paperSpatio-Temporal Graph Convolutional Networks:A Deep Learning Framework for Traffic Forecasting.
https://arxiv.org/abs/1709.04875
@inproceedings{10.5555/3304222.3304273,author = {Yu, Bing and Yin, Haoteng and Zhu, Zhanxing},title = {Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting},year = {2018},isbn = {9780999241127},publisher = {AAAI Press},booktitle = {Proceedings of the 27th International Joint Conference on Artificial Intelligence},pages = {3634–3640},numpages = {7},series = {IJCAI'18}}- TCN:An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
- GLU and GTU:Language Modeling with Gated Convolutional Networks
- ChebNet:Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
- GCN:Semi-Supervised Classification with Graph Convolutional Networks
- TCN:https://github.com/locuslab/TCN
- ChebNet:https://github.com/mdeff/cnn_graph
- GCN:https://github.com/tkipf/pygcn
- METR-LA:DCRNN author's Google Drive
- PEMS-BAY:DCRNN author's Google Drive
- PeMSD7(M):STGCN author's GitHub repository
Using the formula fromChebNet：
- Fix bugs
- Add Early Stopping approach
- Add Dropout approach
- Offer a different set of hyperparameters
- Offer config files for two different categories graph convolution (ChebyGraphConv and GraphConv)
- Add datasets METR-LA and PEMS-BAY
- Adopt a different data preprocessing method
To install requirements:
pip3 install -r requirements.txt