## 4 Experiments
The dynamic graph generation module is used to deal with the dynamics of long-term and local spatial correlations in time series, enabling the model to capture changes in local spatial correlations in the time series in a timely manner. The prediction accuracy is improved by enhancing the dynamic fit of DG-STFN to time series through the dynamic adjustment of the local connection relationship. Moreover, the results in Table 1 show that DG-STFN outperforms the baselines for all prediction tasks on both datasets. In particular, DG-STFN achieved an average improvement of approximately 33.82% compared to SARIMA, 26.20% compared to LSTM, 15.98% compared to STGCN, 13.97% compared to DCRNN, and 4.37% compared to ASTGCN in terms of MAE metric across four different prediction tasks with multiple time steps, on the WSD dataset. To further compare the performance between DG-STFN and the GCN-based baselines, we visualize the predicted and actual results on the test set of the WSD dataset. Specifically, we focus on a one-week time span and intercept the results for comparative analysis, as shown in Figure 2.  
As can be seen from Figure 2, DG-STFN, ASTGCN, and DCRNN predicted values are largely evenly dispersed around the ground truth, while the STGCN predicted values are relatively concentrated. Therefore, it is intuitive that STGCN has the poorest fit to the true values, and that DG-STFN, ASTGCN, and DCRNN have superior fit. The predicted values of the DG-STFN and ground truth are evenly distributed in an interval consisting of the minimum of the ground truth and the maximum of the predicted values. However, ASTGCN, STGCN, and DCRNN all have ground truth distributed outside the interval consisting of the minimum of the ground truth and the maximum of predicted values, indicating that these baselines are not as effective as DG-STFN in predicting data peaks. Further evidence of the validity of the DG-STFN model. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**H = 24, P = 3**} & \multicolumn{2}{c}{**H = 24, P = 6**} & \multicolumn{2}{c}{**H = 24, P = 9**} & \multicolumn{2}{c}{**H = 24, P = 12**} \\ \cline{3-10}  & & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline \multirow{8}{*}{WSD} & SARIMAX & 3.22 & 8.65 & 3.53 & 9.08 & 4.16 & 9.27 & 4.23 & 8.33 \\  & LSTM & 2.99 & 7.23 & 3.32 & 7.90 & 3.59 & 8.68 & 3.39 & 8.23 \\  & DCRNN & 2.55 & 6.70 & 2.83 & 7.19 & 3.03 & 7.41 & 3.10 & 7.47 \\  & STGCN & 2.78 & 7.31 & 2.99 & 7.71 & 2.99 & 7.63 & 2.98 & 7.70 \\  & ASTGCN & 2.52 & 6.73 & 2.56 & 6.92 & 2.61 & 7.04 & 2.62 & 7.06 \\  & **DG-STFN** & **2.38** & **6.69** & **2.47** & **6.85** & **2.51** & **6.97** & **2.50** & **7.01** \\ \hline \multirow{8}{*}{APD} & SARIMAX & 12.36 & 15.62 & 13.50 & 19.97 & 15.12 & 20.22 & 17.68 & 25.30 \\  & LSTM & 10.02 & 15.06 & 12.95 & 18.35 & 14.34 & 19.95 & 15.24 & 21.14 \\ \cline{1-1}  & DCRNN & 9.63 & 14.51 & 12.20 & 17.84 & 13.75 & 19.64 & 15.17 & 21.10 \\ \cline{1-1}  & STGCN & 10.60 & 15.12 & 12.59 & 17.86 & 14.59 & 20.44 & 17.14 & 23.16 \\ \cline{1-1}  & ASTGCN & 9.60 & 14.49 & 12.22 & 17.82 & 13.72 & 19.51 & 14.89 & 20.78 \\ \cline{1-1}  & **DG-STFN** & **9.42** & **14.28** & **12.05** & **17.59** & **13.61** & **19.33** & **14.82** & **20.68** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of different methods.