Fig 1.
Dilated Convolutional Structure.
Fig 2.
Structure of TCN.
Fig 3.
Classification of Ensemble Learning.
Fig 4.
Basic framework of stacking.
Fig 5.
Bi-directional selection layer.
Fig 6.
Structure of GRU.
Fig 7.
Structure of TCN-GRU.
Fig 8.
Research framework for BiStacking+TCN-GRU.
Table 1.
Statistical results of raw data.
Fig 9.
Correlation Heat Map.
Fig 10.
The Impact of Feature Engineering on Prediction Results.
Fig 11.
Comparison of evaluation metrics for the three models.
Table 2.
Comparison of resource consumption of deep learning models.
Fig 12.
Prediction Results of the BiStacking Model.
Fig 13.
Evaluation Metrics for the BiStacking Model Experiment.
Table 3.
Evaluation Metrics for the BiStacking Model Experiment.
Fig 14.
Prediction Results of the TCN-GRU Model.
Fig 15.
Evaluation Metrics for the TCN-GRU Model Experiment.
Table 4.
Evaluation Metrics for the TCN-GRU Model Experiment.
Fig 16.
Predictions of All Models.
Fig 17.
Evaluation Metrics for All Models.
Table 5.
Evaluation metrics for all models.
Fig 18.
Error Bar Experiment.
Fig 19.
Results of the robustness experiment.
Fig 20.
Evaluation metrics of the robustness experiment.
Table 6.
Evaluation metrics for the robustness experiment.