Papers › Graph Neural Rough Differential Equations for Traffic Forecasting
Graph Neural Rough Differential Equations for Traffic Forecasting
Jeongwhan Choi, Noseong Park
Traffic forecasting is one of the most popular spatio-temporal tasks in the field of machine learning. A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing. There has been fierce competition and many novel methods have been proposed. In this paper, we present the method of spatio-temporal graph neural rough differential equation (STG-NRDE). Neural rough differential equations (NRDEs) are a breakthrough concept for processing time-series data. Their main concept is to use the log-signature transform to convert a time-series sample into a relatively shorter series of feature vectors. We extend the concept and design two NRDEs: one for the temporal processing and the other for the spatial processing. After that, we combine them into a single framework. We conduct experiments with 6 benchmark datasets and 27 baselines. STG-NRDE shows the best accuracy in all cases, outperforming all those 27 baselines by non-trivial margins.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Traffic Prediction | PeMSD3 | STG-NRDE | 12 steps MAE | 15.50 | #5 of 6 | Archive leaderboard | report |
| Traffic Prediction | PeMSD3 | STG-NRDE | 12 steps MAPE | 14.9 | #5 of 6 | Archive leaderboard | report |
| Traffic Prediction | PeMSD3 | STG-NRDE | 12 steps RMSE | 27.06 | #5 of 6 | Archive leaderboard | report |
| Traffic Prediction | PeMSD4 | STG-NRDE | 12 steps MAE | 19.13 | #12 of 13 | Archive leaderboard | report |
| Traffic Prediction | PeMSD4 | STG-NRDE | 12 steps MAPE | 12.68 | #12 of 13 | Archive leaderboard | report |
| Traffic Prediction | PeMSD4 | STG-NRDE | 12 steps RMSE | 30.94 | #12 of 13 | Archive leaderboard | report |
| Traffic Prediction | PeMSD7 | STG-NRDE | 12 steps MAE | 20.45 | #7 of 8 | Archive leaderboard | report |
| Traffic Prediction | PeMSD7 | STG-NRDE | 12 steps MAPE | 8.65 | #7 of 8 | Archive leaderboard | report |
| Traffic Prediction | PeMSD7 | STG-NRDE | 12 steps RMSE | 33.73 | #7 of 8 | Archive leaderboard | report |
| Traffic Prediction | PeMSD7(L) | STG-NRDE | 12 steps MAE | 2.85 | #5 of 6 | Archive leaderboard | report |
| Traffic Prediction | PeMSD7(L) | STG-NRDE | 12 steps MAPE | 7.14 | #5 of 6 | Archive leaderboard | report |
| Traffic Prediction | PeMSD7(L) | STG-NRDE | 12 steps RMSE | 5.76 | #5 of 6 | Archive leaderboard | report |
| Traffic Prediction | PeMSD7(M) | STG-NRDE | 12 steps MAE | 2.66 | #5 of 7 | Archive leaderboard | report |
| Traffic Prediction | PeMSD7(M) | STG-NRDE | 12 steps MAPE | 6.68 | #5 of 7 | Archive leaderboard | report |
| Traffic Prediction | PeMSD7(M) | STG-NRDE | 12 steps RMSE | 5.31 | #5 of 7 | Archive leaderboard | report |
| Traffic Prediction | PeMSD8 | STG-NRDE | 12 steps MAE | 15.32 | #12 of 13 | Archive leaderboard | report |
| Traffic Prediction | PeMSD8 | STG-NRDE | 12 steps MAPE | 8.9 | #12 of 13 | Archive leaderboard | report |
| Traffic Prediction | PeMSD8 | STG-NRDE | 12 steps RMSE | 24.72 | #12 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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