A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting

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Benchmark Model Rank Results
traffic-prediction-on-pems04DDGCRN#712 Steps MAE: 18.45
traffic-prediction-on-pems07DDGCRN#9MAE@1h: 19.79
traffic-prediction-on-pems08DDGCRN#11MAE@1h: 14.40
traffic-prediction-on-pemsd3DDGCRN#312 steps MAE: 14.6312 steps MAPE: 14.2212 steps RMSE: 25.07
traffic-prediction-on-pemsd4DDGCRN#712 steps MAE: 18.45
traffic-prediction-on-pemsd7DDGCRN#512 steps MAE: 19.79
traffic-prediction-on-pemsd7-lDDGCRN#212 steps MAE: 2.7912 steps MAPE: 7.0612 steps RMSE: 5.68
traffic-prediction-on-pemsd7-mDDGCRN#212 steps MAE: 2.5912 steps MAPE: 6.4812 steps RMSE: 5.21
traffic-prediction-on-pemsd8DDGCRN#912 steps MAE: 14.40

Rank counts only results with a code link.