Code for the paper "Multivariate Time Series Prediction of Complex Systems Based on Graph Neural Networks with Location Embedding Graph Structure Learning"
This is a Pytorch implementation.
- scipy>=1.7.0
- numpy>=1.21.0
- pandas>=1.2.5
- pyaml
- statsmodels
- pytorch>=1.8.0
- networkx>=2.6.3
The traffic flow dataset has been placed in the dataset folder of the code. You need to unzip the dataset to this folder first. METR-LA and PEMS-BAY source and original paper of DCRNN. PEMSD4 and PEMSD8 come from the paper ASTGCN.
You need to first specify the dataset name in main.py and then directly to run
python main.pyPlease wait patiently for the program to finish running.
Note: The Model is not designed for traffic flow prediction, but performs well on the traffic flow dataset. Baseline code: STGCN, DCRNN, MTGNN, GMAN, STGNN, GTS, Ada-STNet, STFGNN, GraphWaveNet, ASTGCN, STSGCN, AGCRN, Z-GCNETs, DSTAGNN, STG-NCDE.
| Dataset | METR-LA | PEMS-BAY | ||||
|---|---|---|---|---|---|---|
| Baseline | MAE | MAPE(%) | RMSE | MAE | MAPE(%) | RMSE |
| STGCN | 4.45 | 11.8 | 8.41 | 2.49 | 5.69 | 5.79 |
| DCRNN | 3.6 | 10.5 | 7.59 | 2.07 | 4.74 | 4.9 |
| MTGNN | 3.49 | 9.87 | 7.23 | 1.94 | 4.53 | 4.49 |
| GMAN | 3.48 | 10.1 | 7.3 | 1.86 | 4.32 | 4.31 |
| STGNN | 3.49 | 9.69 | 6.94 | 1.83 | 4.15 | 4.2 |
| GTS | 3.41 | 9.9 | 6.74 | 1.91 | 4.4 | 3.97 |
| Ada-STNet | 3.47 | 9.8 | 7.18 | 1.89 | 4.5 | 4.36 |
| STFGNN(SOTA) | 3.18 | 8.81 | 6.4 | 1.66 | 3.77 | 3.74 |
| ours | 3.16 | 8.83 | 6.38 | 1.64 | 3.68 | 3.72 |
| Dataset | PEMSD4 | PEMSD8 | ||||
| Baseline | MAE | MAPE(%) | RMSE | MAE | MAPE(%) | RMSE |
| STGCN | 21.16 | 13.83 | 35.69 | 17.5 | 11.29 | 27.09 |
| DCRNN | 21.22 | 14.17 | 37.23 | 16.82 | 10.92 | 26.36 |
| GraphWaveNet | 28.15 | 18.52 | 39.88 | 20.3 | 13.84 | 30.82 |
| ASTGCN | 22.93 | 16.56 | 34.33 | 18.25 | 11.64 | 28.06 |
| MSTGCN | 23.96 | 14.33 | 37.21 | 19 | 12.38 | 29.15 |
| STSGCN | 21.19 | 13.9 | 33.69 | 17.13 | 10.96 | 26.86 |
| STFGNN | 19.83 | 13.02 | 31.88 | 16.64 | 10.6 | 26.22 |
| AGCRN | 19.83 | 12.97 | 32.3 | 15.95 | 10.09 | 25.22 |
| Z-GCNETs | 19.5 | 12.78 | 31.61 | 15.76 | 10.01 | 25.11 |
| DSTAGNN | 19.3 | 12.7 | 31.46 | 15.67 | 9.94 | 24.77 |
| STG-NCDE(SOTA) | 19.21 | 12.76 | 31.09 | 15.45 | 9.92 | 24.81 |
| ours | 19.15 | 12.46 | 31.15 | 15.44 | 9.54 | 24.56 |
Shi, X., et al. (2022). "Multivariate time series prediction of complex systems based on graph neural networks with location embedding graph structure learning." Advanced Engineering Informatics 54: 101810.