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zezhishao / STEP

Code for our SIGKDD'22 paper Pre-training-Enhanced Spatial-Temporal Graph Neural Network For Multivariate Time Series Forecasting.

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About STEP-DCRNN

Jimmy-7664 opened this issue · comments

commented

I noticed that step-dcrnn experiments appear in the ablation experiments section, how did you incorporate step into dcrnn? A code example would be helpful.

commented

The specific method is to fuse the representation generated by TSFormer with the Encoder of DCRNN, and the others are the same as Graph WaveNet.

commented

Now I don't have the code too, I will reproduce it later.

commented

See branch dev/dcrnn.
This is the result of the first 50 epochs on the METR-LA dataset:

2023-07-29 05:33:08,260 - easytorch-training - INFO - Result <train>: [train_time: 312.44 (s), lr: 1.00e-04, train_MAE: 2.7313, train_RMSE: 5.4791, train_MAPE: 0.0720]
2023-07-29 05:33:08,261 - easytorch-training - INFO - Start validation.
2023-07-29 05:34:07,963 - easytorch-training - INFO - Result <val>: [val_time: 59.70 (s), val_MAE: 2.7455, val_RMSE: 5.2874, val_MAPE: 0.0748]
2023-07-29 05:34:09,407 - easytorch-training - INFO - Checkpoint checkpoints/STEP_100/506878ab44e96728aec144de7098d6cb/STEP_best_val_MAE.pt saved
2023-07-29 05:36:09,617 - easytorch-training - INFO - Evaluate best model on test data for horizon 1, Test MAE: 2.1745, Test RMSE: 3.7863, Test MAPE: 0.0521
2023-07-29 05:36:09,621 - easytorch-training - INFO - Evaluate best model on test data for horizon 2, Test MAE: 2.4548, Test RMSE: 4.5666, Test MAPE: 0.0612
2023-07-29 05:36:09,625 - easytorch-training - INFO - Evaluate best model on test data for horizon 3, Test MAE: 2.6419, Test RMSE: 5.0937, Test MAPE: 0.0681
2023-07-29 05:36:09,628 - easytorch-training - INFO - Evaluate best model on test data for horizon 4, Test MAE: 2.7905, Test RMSE: 5.5172, Test MAPE: 0.0742
2023-07-29 05:36:09,632 - easytorch-training - INFO - Evaluate best model on test data for horizon 5, Test MAE: 2.9136, Test RMSE: 5.8632, Test MAPE: 0.0794
2023-07-29 05:36:09,637 - easytorch-training - INFO - Evaluate best model on test data for horizon 6, Test MAE: 3.0203, Test RMSE: 6.1615, Test MAPE: 0.0839
2023-07-29 05:36:09,641 - easytorch-training - INFO - Evaluate best model on test data for horizon 7, Test MAE: 3.1131, Test RMSE: 6.4187, Test MAPE: 0.0879
2023-07-29 05:36:09,644 - easytorch-training - INFO - Evaluate best model on test data for horizon 8, Test MAE: 3.1937, Test RMSE: 6.6351, Test MAPE: 0.0914
2023-07-29 05:36:09,648 - easytorch-training - INFO - Evaluate best model on test data for horizon 9, Test MAE: 3.2655, Test RMSE: 6.8282, Test MAPE: 0.0946
2023-07-29 05:36:09,652 - easytorch-training - INFO - Evaluate best model on test data for horizon 10, Test MAE: 3.3307, Test RMSE: 7.0007, Test MAPE: 0.0974
2023-07-29 05:36:09,655 - easytorch-training - INFO - Evaluate best model on test data for horizon 11, Test MAE: 3.3905, Test RMSE: 7.1538, Test MAPE: 0.1000
2023-07-29 05:36:09,658 - easytorch-training - INFO - Evaluate best model on test data for horizon 12, Test MAE: 3.4496, Test RMSE: 7.3009, Test MAPE: 0.1025
2023-07-29 05:36:09,762 - easytorch-training - INFO - Result <test>: [test_time: 120.35 (s), test_MAE: 2.9782, test_RMSE: 6.1186, test_MAPE: 0.0827]
2023-07-29 05:36:10,938 - easytorch-training - INFO - Checkpoint checkpoints/STEP_100/506878ab44e96728aec144de7098d6cb/STEP_034.pt saved
commented

Thank you for your prompt reply, my questions have been answered. Wish you all the best :)