## 4 Experiments
### Ablation Analysis  
We conducted ablation tests to validate the effectiveness of the HyMePre design. Five ablation variants were evaluated on temperature, cloud cover, humidity, and wind datasets. The implemented model variants are described as follows:  
1. w/o S-Mask: Removes temporal masking while retaining spatial masking.
2. w/o T-Mask: Disables spatial masking in pretraining, using only temporal masking.
3. w/o G: Replaces hypergraph convolution with standard GCN layers.
4. w/o Fusion: Replaces the attention fusion module with a simple addition operation for feature fusion.  
Table 4 summarizes the performance degradation patterns across different variants. The full model achieves optimal results on all prediction tasks, demonstrating the necessity of each component. Removing the spatial masking component (w/o S-Mask) leads to a significant drop in prediction accuracy for cloud cover and wind speed. These variables rely on coordinated variations across multiple stations, such as those observed in frontal systems, where inferring missing data requires information from neighboring sites. The absence of temporal masking (w/o T-Mask) weakens the model's ability to capture periodic humidity dynamics, such as the diurnal evaporation cycle, highlighting the importance of temporal continuity reconstruction.  
Replacing hypergraph convolution with conventional graph convolution (w/o G) results in a performance decline across all variables, with the most notable impact on wind speed and cloud cover. Traditional graph structures are limited to pairwise relationships and fail to represent collective behaviors among multiple stations, such as regional wind field propagation. This confirms the essential role of hypergraphs in modeling higher-order spatial dependencies in meteorological phenomena.  
Substituting attention-based fusion with simple addition (w/o Fusion) reduces the model's ability to integrate global and local features effectively. For instance, in temperature prediction, the model struggles to balance localized terrain effects with large-scale atmospheric circulation. In contrast, the fusion mechanism enables dynamic weighting across spatial scales, enhancing the representation of complex weather systems.  
The ablation study highlights that each component contributes uniquely to the overall effectiveness of the framework. Excluding any module results in noticeable performance drops, demonstrating that the system's carefully designed architecture enables distinct yet complementary roles. This coordinated interaction among modules is fundamental to the model's strong predictive capabilities in meteorological applications.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Temperature**} & \multicolumn{2}{c}{**Cloud Cover**} & \multicolumn{2}{c}{**Humidity**} & \multicolumn{2}{c}{**Wind**} \\ \cline{2-9}  & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline w/o S-Mask & 1.4682 & 2.0423 & 0.2218 & 0.3296 & 5.2357 & 8.0124 & 1.7851 & 2.3940 \\ w/o T-Mask & 1.4597 & 2.0175 & 0.2184 & 0.3251 & 5.1983 & 7.9886 & 1.7729 & 2.3756 \\ w/o G & 1.4910 & 2.0850 & 0.2942 & 0.3319 & 5.2941 & 8.0317 & 1.8020 & 2.4208 \\ w/o-Fusion & 1.5326 & 2.8652 & 0.2275 & 0.4156 & 5.4172 & 8.2415 & 1.8645 & 2.4815 \\ Our & **1.4245** & **1.9762** & **0.2137** & **0.3171** & **5.1723** & **7.9656** & **1.7286** & **2.3369** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation analysis. Results in bold indicate the best overall performance.