Traffic Prediction On Pemsd7 L
Metrics
12 steps MAE
12 steps MAPE
12 steps RMSE
Results
Performance results of various models on this benchmark
Model Name | 12 steps MAE | 12 steps MAPE | 12 steps RMSE | Paper Title | Repository |
|---|---|---|---|---|---|
| DDGCRN | 2.79 | 7.06 | 5.68 | A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting | |
| STD-MAE | 2.64 | 6.65 | 5.50 | Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting | |
| STG-NCDE | 2.87 | 7.31 | 5.76 | Graph Neural Controlled Differential Equations for Traffic Forecasting | |
| PM-DMNet(R) | 2.79 | 6.99 | 5.81 | Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction | |
| STG-NRDE | 2.85 | 7.14 | 5.76 | Graph Neural Rough Differential Equations for Traffic Forecasting | |
| PM-DMNet(P) | 2.81 | 7.13 | 5.79 | Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction |
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