## 5 Experiment
### Result  
As shown in Table 2, we found that, on average, 3.72% performance improvement occurred in the Call sub-dataset when using non-local attention using Gaussian kernels applied to Triplet attention, 5.87% was found in the Internet sub-dataset when using non-local attention with the softmax function, and 2.84% was found in the SMS sub-dataset when using spatial attention with kernel size of 5. Especially, when the model uses non-local attention using softmax function, it shows relatively poor performance on the Call and SMS sub-datasets while achieving the best performance on the Internet sub-dataset. This points out that the effectiveness of attention in ADNet may vary depending on the datasets. As shown in Table 3, although our method performed worse than HSTNet by about 14.14% in the Internet sub-dataset, we found that, on average, 28.42% performance improvement occurred in the Call sub-dataset when using triplet attention with kernel size 5, and 6.62% in the SMS sub-dataset when using non-local attention with the softmax function. Unlike the experiment conducted with 20 \(\times\) 20 grid, we found that ADNet using non-local attention with the softmax function performs the best on both the SMS and Internet sub-datasets among our methods. We think this is because modifying the sub-datasets from a pair of channels to a single channel altered their characteristics. As shown in Table 4, although our method is worse than MVSTGN in the Internet sub-dataset, like in Table 3, we found that, on average, 10.00% performance improvement occurred in the Call sub-dataset and 8.61% in the SMS sub-dataset when using CBAM module with kernel size 3. As shown in Tables 5 and 6, our method performs noticeably worse than existing methods in SMAPE, unlike RMSE. This suggests that our approach has more difficulty predicting numerically small values as the propotion of such data increases. However, as shown in Table 6, SMAPE was improved compared with the baseline, which indicates that the attention module clearly contributes to performance enhancement. Only partial results are shown for visibility. Full results, including MAE evaluation, are provided in the Supplementary Materials. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Attention** & **Call** & **Internet** & **SMS** \\ \hline STDenseNet (baseline) & 16.17 (0.85) & 172.48 (9.03) & 26.76 (1.02) \\ MVSTGN & 175.33 (17.07) & 198.04 (25.21) & 31.18 (6.69) \\ \hline ADNet w. 3 \(\times\) 3 Spatial & 15.90 (1.11) & 171.73 (10.34) & 26.36 (1.44) \\ ADNet w. 5 \(\times\) 5 Spatial & 15.84 (1.06) & 170.50 (7.37) & **26.02 (1.33)** \\ ADNet w. 7 \(\times\) 7 Spatial & 16.05 (1.07) & 170.09 (6.53) & 26.36 (1.21) \\ \hline ADNet w. Channel & 16.10 (1.24) & 174.25 (15.10) & 26.72 (1.58) \\ \hline ADNet w. 3 \(\times\) 3 CBAM & 16.76 (1.79) & 181.30 (19.40) & 27.22 (2.65) \\ ADNet w. 5 \(\times\) 5 CBAM & 16.71 (1.86) & 181.78 (21.23) & 26.95 (2.94) \\ ADNet w. 7 \(\times\) 7 CBAM & 16.39 (1.54) & 180.03 (20.11) & 27.02 (2.73) \\ \hline ADNet w. 3 \(\times\) 3 Triplet & 15.77 (0.76) & 168.03 (6.14) & 26.39 (0.97) \\ ADNet w. 5 \(\times\) 5 Triplet & 15.68 (0.76) & 168.11 (5.91) & 26.41 (1.04) \\ ADNet w. 7 \(\times\) 7 Triplet & 15.77 (0.89) & 168.72 (6.77) & 26.37 (1.01) \\ \hline ADNet w. Non-local Softmax & 16.24 (1.84) & **162.92 (8.48)** & 27.49 (4.08) \\ ADNet w. Non-local Gaussian & 15.75 (0.81) & 167.40 (7.04) & 26.32 (1.09) \\ \hline ADNet w. Triplet Non-local Softmax & 15.81 (1.49) & 166.38 (10.14) & 28.17 (3.35) \\ ADNet w. Triplet Non-local Gaussian & **15.59 (0.73)** & 170.23 (7.59) & 26.35 (0.95) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RMSE comparison of STDenseNet with different attentions in 20 \(\times\) 20 grid. The number in brackets denotes standard deviation. The bold numbers indicate the best performances in the table.