Research map / Time series / Long-term forecasting
Time-Conditioned Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use ZFC-SHCN instead of Z-GCNETs (zigzag persistence image) | Z-GCNETs · PeMSD4 | RMSE: 29.08 -> 28.58 | Helped |
| use ZFC-SHCN instead of Z-GCNETs | Z-GCNETs · PeMSD8 | RMSE: 23.00 -> 21.83 | Helped |
| use ZFC-SHCN instead of Z-GCNETs | Z-GCNETs · COVID-19 TX | RMSE: 46.04 -> 27.81 | Helped |
| use ZFC-SHCN instead of AGCRN, StemGNN, Z-GCNETs | strongest baselines · VART1 | RMSE: 0.49 -> 0.45 | Helped |
| use ZFC-SHCN instead of Z-GCNETs | Z-GCNETs · VART2 | RMSE: 0.44 -> 0.38 | Helped |
| compare with TAMP-S2GCNets multipersistence GNN | ZFC-SHCN · traffic and COVID benchmarks | accuracy and runtime | Mixed |
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