## 4 Experimentation
### Experimental Results
#### 4.5.1 Comparison Experiment  
The experimental results of the three datasets indicate that temporal knowledge graph completion methods generally outperform static methods. This is because TKG contains temporal information between facts, which provides crucial contextual information for time-sensitive link prediction tasks. Additionally, TKG can capture the patterns of facts changing over time, such as adding or deleting facts, which helps predict missing entities or relationships. HCAE demonstrates significant performance improvements compared with the four static methods. For example, compared with the best-performing static method R-GCN, HCAE achieves improvements of 10.1, 14.6, and 19.6 in Hits@1/3/10 on the GDELT dataset. This highlights the effectiveness of modeling temporal dependencies between facts. The HCAE model was compared with several TKGC methods on the GDELT dataset, and from Table 3, it can be observed that HCAE outperformed TTransE by significant margins in all four metrics. Specifically, HCAE achieved improvements of 24.6, 27.3, 23.6, and 22.2 percentage points in the respective metrics. Because TTransE only models the transla \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**GDELT**} & \multicolumn{4}{c}{**ICEWS14**} & \multicolumn{4}{c}{**ICEWS05-15**} \\ \cline{2-13}  & **MRR** & **H@1** & **H@3** & **H@10** & **MRR** & **H@1** & **H@3** & **H@10** & **MRR** & **H@1** & **H@3** & **H@10** \\ \hline TransE & 11.3 & 0.0 & 15.8 & 31.2 & 28.0 & 9.4 & - & 63.7 & 29.4 & 9.0 & - & 66.3 \\ DistMult & 19.6 & 11.7 & 20.8 & 34.8 & 43.9 & 32.3 & - & 67.2 & 45.6 & 33.7 & - & 69.7 \\ ComplEx & 22.8 & 15.8 & 24.0 & 36.3 & 24.5 & 16.1 & 27.5 & 41.1 & - & - & - & - \\ R-GCN & 23.3 & 17.2 & 25.0 & 34.4 & 26.3 & 18.2 & 30.4 & 45.3 & - & - & - & - \\ \hline TTransE & 11.5 & 0.0 & 16.0 & 31.8 & 25.5 & 7.4 & - & 60.1 & 27.1 & 8.4 & - & 61.6 \\ TA-DistMult & 20.6 & 12.4 & 21.9 & 36.5 & 47.7 & 36.3 & - & 68.6 & 47.4 & 34.6 & - & 72.8 \\ ChronoR(a) & - & - & - & - & 59.4 & 49.6 & 65.4 & 77.3 & 68.4 & 61.1 & 73.0 & 82.1 \\ ChronoR(b) & - & - & - & - & **62.5** & 54.7 & 66.9 & 77.3 & 67.5 & 59.6 & 72.3 & 82.0 \\ TNTComplEx & - & - & - & - & 62.0 & 52.0 & 66.0 & 76.0 & 67.0 & 59.0 & 71.0 & 81.0 \\ TeMP-SA & 23.2 & 15.2 & 24.5 & 37.7 & 60.7 & 48.4 & 68.4 & **84.0** & 68.0 & 55.3 & 76.9 & 91.3 \\ TeMP-GRU & 27.5 & 19.1 & 29.7 & 43.7 & 60.1 & 47.8 & 68.1 & 82.5 & 69.1 & 56.6 & 78.2 & **91.7** \\ MtGCN & 23.4 & 14.7 & 25.9 & 40.7 & 35.0 & 25.8 & 38.9 & 53.0 & - & - & - & - \\ BoxTE & 35.2 & 26.9 & 37.7 & 51.1 & 61.3 & 52.8 & 66.4 & 76.3 & 66.7 & 58.2 & 71.9 & 82.0 \\ RoAN-DES & 29.0 & 18.7 & 31.5 & 49.6 & 58.8 & 47.6 & 66.1 & 78.8 & 59.9 & 47.9 & 67.9 & 82.3 \\ RotateQVS & 27.0 & 17.5 & 29.3 & 45.8 & 59.1 & 50.7 & 64.2 & 75.4 & 63.3 & 52.9 & 70.9 & 81.3 \\ TASTER & - & - & - & - & 61.1 & 52.7 & - & 76.7 & 65.4 & 56.2 & - & 81.8 \\ \hline HCAE & **36.1** & **27.3** & **39.6** & **54.0** & 62.1 & **54.9** & **68.7** & 83.7 & **71.2** & **62.5** & **78.4** & 91.3 \\ \hline \hline \end{tabular} Note that—indicates that the original paper did not report results. Bold indicates the best results.  
\end{table} 
Table 3: Comparison of prediction results on the GDELT, ICEWS14, and ICEWS05-15 datasets. tion operations of entities and relations, it cannot effectively model complex event evolution. Compared with TA-DistMult, HCAE achieved 15.5, 14.9, 17.7, and 17.5 improvements in the respective metrics. This is primarily because TA-DistMult relies on matrix factorization, which cannot handle large-scale datasets. Additionally, TeV-SA and TeV-GRU struggle to model complex long-term dependencies due to their reliance on temporal ordering information. Furthermore, MtGCN only considers direct relationships between entities, disregarding the rich attribute information present in TKG. These factors contribute to the significant differences in results when compared with HCAE.