Research map / Graph learning / Knowledge and temporal graphs
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add spatial-temporal attention to multi-component graph conv network (ablation) | MSTGCN · PeMSD4 | RMSE: 35.64 -> 32.82 | Helped |
| add spatial-temporal attention to multi-component graph conv network (ablation) | MSTGCN · PeMSD8 | MAE: 17.47 -> 16.63 | Helped |
| ASTGCN vs best prior baseline GeoMAN | traffic forecasting · PeMSD4 | RMSE: 37.84 -> 32.82 | Helped |
| increase Chebyshev order K from 1 to 3 (and temporal kernel size) (ablation) | ASTGCN · PeMS | forecast error | Helped |
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