## 4 Experiments
### Performance Comparison  
Table 1 demonstrates the comparison results of our proposed HyMePre method with other mainstream spatiotemporal graph neural networks on four different meteorological datasets. The specific evaluation metrics include the mean absolute error (MAE) and root mean square error (RMSE) for temperature, cloud cover, humidity, and wind. Smaller values of MAE and RMSE indicate lower prediction errors and better model performance.  
As shown in Table 1, HyMePre achieves strong performance across various meteorological variables, particularly in tasks that require modeling complex spatial interactions. The integration of hypergraph neural networks with a decoupled spatiotemporal pretraining approach enables the model to effectively capture higher-order dependencies. This is especially advantageous for variables like wind and cloud patterns, where multi-station correlations play a key role.  
By leveraging hyperedges to represent group-level atmospheric behaviors, HyMePre outperforms traditional graph-based methods that rely on pairwise connections, enabling more comprehensive spatial information propagation. The self-supervised pretraining strategy further enhances the model's ability to generalize, particularly under noisy or incomplete observational conditions.  
To ensure the robustness and reliability of these findings, we include error bars in Table 2 and confidence intervals in Table 3. These statistical measures provide a clearer picture of the variability and uncertainty in model performance, supporting more confident comparisons among methods.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Temperature**} & \multicolumn{2}{c}{**Cloud**} & \multicolumn{2}{c}{**Humidity**} & \multicolumn{2}{c}{**Wind**} \\ \cline{2-9}  & **MAE \(\pm\) CI** & **RMSE \(\pm\) CI** & **MAE \(\pm\) CI** & **RMSE \(\pm\) CI** & **MAE \(\pm\) CI** & **RMSE \(\pm\) CI** & **MAE \(\pm\) CI** & **RMSE \(\pm\) CI** \\ \hline HA & 8.7139 \(\pm\) 0.2132 & 12.1906 \(\pm\) 0.2293 & 0.3218 \(\pm\) 0.0074 & 0.4443 \(\pm\) 0.0064 & 14.1265 \(\pm\) 0.0611 & 19.2899 \(\pm\) 0.1361 & 5.4621 \(\pm\) 0.0982 & 6.8627 \(\pm\) 0.0305 \\ ARIMA & 5.5379 & 68.4896 & 0.3036 & 0.5157 & 7.5307 & 15.0790 & 6.1104 & 7.1914 \\ RNN & 5.7892 & 7.7893 & 0.2827 & 0.4289 & 9.1931 & 12.6847 & 3.0875 & 4.2609 \\ STGCN & 5.0880 & 6.9926 & 0.2897 & 0.3777 & 8.4517 & 8.7509 & 5.0770 & 6.4942 \\ ASTGCN & 2.2724 & 3.0595 & 0.2890 & 0.3817 & 7.0583 & 9.6149 & 2.8986 & 4.2152 \\ AGCRN & 1.9150 & 2.2956 & 0.2340 & 0.3368 & 6.1040 & 9.6839 & 3.1805 & 4.4690 \\ SimpleSTG & **1.3500** & **1.9323** & 0.2241 & 0.3235 & 5.2221 & 7.9983 & 3.5102 & 5.0145 \\ FCSTGNN & 1.5854 & 2.1233 & 0.2390 & 0.3403 & 5.4984 & 8.4083 & 2.0154 & 3.1257 \\ \hline HeMePre & 1.4205 & 1.9777 & **0.2132** & **0.3128** & **5.1625** & **7.9361** & **1.7176** & **2.3126** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of our method with mainstream spatiotemporal graph neural networks on four weather datasets. Results in bold indicate the best overall performance. The result underlined indicates the second-best overall performance.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Temperature**} & \multicolumn{2}{c}{**Cloud**} & \multicolumn{2}{c}{**Humidity**} & \multicolumn{2}{c}{**Wind**} \\ \cline{2-9}  & **MAE \(\pm\) CI** & **RMSE \(\pm\) CI** & **MAE \(\pm\) CI** & **RMSE \(\pm\) CI** & **MAE \(\pm\) CI** & **RMSE \(\pm\) CI** & **MAE \(\pm\) CI** & **RMSE \(\pm\) CI** \\ \hline HA & 8.7139 \(\pm\) 0.2132  
While the model may be less optimal for variables influenced by highly localized features, such as temperature, the spatial masking mechanism helps to address this to some extent. Overall, HyMePre presents a promising framework with broad applicability, and future work may further refine its adaptability and efficiency for operational use.