Traffic Prediction On Pemsd7 M
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 |
|---|---|---|---|---|---|
| PM-DMNet(R) | 2.60 | 6.57 | 5.36 | Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction | |
| STG-NCDE | 2.68 | 6.76 | 5.39 | Graph Neural Controlled Differential Equations for Traffic Forecasting | |
| PM-DMNet(P) | 2.61 | 6.55 | 5.33 | Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction | |
| STD-MAE | 2.52 | 6.35 | 5.20 | Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting | |
| STGM | 3.002 | 8.01 | 6.331 | Spatio-Temporal Graph Mixformer for Traffic Forecasting | |
| STG-NRDE | 2.66 | 6.68 | 5.31 | Graph Neural Rough Differential Equations for Traffic Forecasting | |
| DDGCRN | 2.59 | 6.48 | 5.21 | A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting |
0 of 7 row(s) selected.