Traffic Prediction On Pemsd7
Métriques
12 steps MAE
12 steps MAPE
12 steps RMSE
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | 12 steps MAE | 12 steps MAPE | 12 steps RMSE | Paper Title | Repository |
|---|---|---|---|---|---|
| STG-NCDE | 20.53 | 8.8 | 33.84 | Graph Neural Controlled Differential Equations for Traffic Forecasting | |
| PDFormer | 19.832 | - | - | PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction | |
| PM-DMNet(R) | 19.18 | 7.95 | 33.15 | Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction | |
| STAEformer | 19.14 | 8.01 | 32.60 | STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting | |
| DDGCRN | 19.79 | - | - | A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting | |
| PM-DMNet(P) | 19.35 | 8.05 | 33.33 | Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction | |
| STG-NRDE | 20.45 | 8.65 | 33.73 | Graph Neural Rough Differential Equations for Traffic Forecasting | |
| STD-MAE | 18.31 | 7.72 | 31.07 | Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting |
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