## 3 Experiments
### Comparative Experiments  
A consistent training and testing method of equal granularity was adopted to test the models' performance across tasks with varying forecasting granularities. The test results are shown in Table 3.  
In the 15-min prediction scenario, the MAPE metric improves by 2.11% compared with the optimal baseline model, ASTGCN, with an improvement of about 36.1%; in the 45-min prediction scenario, the MAPE metric improves by 4.33% compared with the optimal baseline model, ASTGCN, with an improvement of about 29.3%. In the 90-min prediction scenario, the MAPE metric improves by 6.32% compared with the optimal baseline model. In the 90 min prediction scenario, compared with the optimal baseline model, ASTGCN, the MAPE index improves by 6.32%, about 35%. Compared to the 15-min forecasting scenario, longer forecasting granularities resulted in the EG-DGATN model's MAPE metrics decreasing by 6.46% and 7.68%, respectively. In contrast, ASTGCN decreased by 8.68% and 12.74%, GraphWave decreased by 7.78% and 11.45%, and GNNLSTM decreased by 7.72% and 9.75%, respectively. This result demonstrates the stability of the EG-DGATN model's performance across different forecasting granularities. The EG-DGATN has excellent performance at different forecasting granularities because the periodicity of the time series over different periods is extracted in the modeling using EEMD, and cross-temporal granularity modeling is achieved more efficiently through causal domain graph construction. According to the results presented in Table 3, the ARIMA  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**15 min**} & \multicolumn{4}{c}{**45 min**} & \multicolumn{4}{c}{**90 min**} & \multirow{2}{*}{**Parameters**} \\ \cline{2-2} \cline{5-11}  & **MAE** & **RMSE** & **MAPE** & & & & & & & & \\ \hline ARIMA & 274.63 & 489 & 24.01\% & 0.78 & 458.36 & 769.68 & 36.78\% & 0.74 & 68  
method based on statistics performed the poorest on the dataset due to the complexity of the nonlinear relationships in the training data. However, during the 15 min granularity test, both GraphWave and ASTGCN methods outperformed other spatiotemporal convolution models that rely on predefined adjacency matrices. This suggests that optimizing and learning the connectivity of sensor nodes are crucial for improving predictive performance. Compared with the denser matrix generated by ASTGCN and GraphWave, the dynamic graph multi-head attention used by the EG-DGATN can effectively regularize the scope of the information transfer, so that the information is transferred along the direction that is beneficial to the model performance, which significantly improves the model's performance. Moreover, the LSTM-based GNNLSTM model effectively reduces gradient explosion and error propagation through the forgetting mechanism, which significantly improves the performance in long time series prediction tasks. The EG-DGATN model, employing the Transformer architecture, does not rely on stacking prediction results to achieve predictions at multiple levels of detail. Instead, its strength lies in the dynamic graph generation module, which adeptly manages both long-term trends and local spatial correlations within the time series data. This approach allows the model to quickly adapt to changes in local sensor correlations, significantly enhancing its predictive accuracy compared to traditional baseline models. It is worth noting that, to validate the improvement in model performance resulting from the Granger causality testing graph construction method, this experiment included two special baselines: R-DGATN and S-DGATN. The effectiveness of the R-DGATN model using randomly generated adjacency matrices decreased by 4.63%, 6.51%, and 7.23% at various time granularities, while the effectiveness of the S-DGATN model using traditional spatial adjacency matrices decreased by 4.88%, 5.56%, and 7.32% at various time granularities.