This is the code for our publication "Understanding and Simplifying Architecture Search in Spatio-Temporal Graph Neural Networks" accepted by Transactions on Machine Learning Research (TMLR) 2023.
- PeMS03/04/07/08
These four datasets are available from Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting
- NE-BJ
This dataset is available from Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution
The default data path is ../../traffic/XXX as by data.py.
We use minimal environment requirement as below. Our code is tested on CUDA 11.1
numpy
pandas
torch==1.9.0
cd SimpleSTG
python run.py
More configurations other than default can be found in run.py.