## 4 Results and Discussion
### Experimental Results  
The evaluation results comprise numerical comparisons using MAE, RMSE, and R\({}^{2}\), as well as performance displays, between DBSTGNN-Att and the five other prediction models. These comparisons were based on the cellular traffic dataset of Milan. The prediction was compared in three different scenarios, namely short-term prediction, mid-term prediction, and long-term predictions, with sampling intervals of 10 min, 30 min, and 60 min, respectively. When the sampling interval was 10 min, the model predicted the cellular traffic in the next one day; when the sampling interval was 30 min, the model predicted the cellular traffic in the next three days; and when the sampling interval was \begin{table}
\begin{tabular}{c c} \hline \hline
**Model Component** & **Parameter** \\ \hline Learning rate & 0.001 \\ Batch size & 32 \\ Training epoch & 1500 \\ Optimizer & Adam \\ K & 10 \\ \(\omega_{0}\), \(\omega_{1}\), \(\omega_{2}\) & 0.4, 0.4, 0.2 \\ Output dimension of the input layer & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model parameter settings. 60 min, the model predicted the cellular traffic in the next six days. In order to demonstrate the prediction performance of DBSTGNN-Att across various sampling intervals of cellular traffic, a representative cell (ID: 4259), which contains the most famous university in Milan named Bocconi, was selected to exhibit the prediction results of the test dataset. Figure 6 provides an intuitive representation of the prediction comparisons for the last 144 time points, representing short-term (one day) and mid-term (three days) predictions, in the selected cell. Furthermore, the numerical comparisons for the aforementioned prediction performance are presented in Table 3 (Note: The results shown in this section do not use the attributed KNN graph to fuse multi-modal features). In the short-term prediction scenario, compared to the GRU, T-GCN, and STA-GCN, DBSTGNN-Att presents performance improvements of around 23.96%, 6.94%, and 2.11% in the MAE and 8.96%, 6.87%, and 1.89% in the RMSE, respectively. In the mid-term prediction scenario, compared to the GRU, T-GCN, and STA-GCN, DBSTGNN-Att presents performance improvements of around 31.83%, 10.07%, and 5.74% in the MAE and 17.67%, 13.05%, and 7.80% in the RMSE, respectively. In the long-term prediction scenario, compared to the GRU, T-GCN, and STA-GCN, DBSTGNN-Att presents performance improvements of around 36.28%, 11.94%, and 5.16% in the MAE and 28.71%, 15.24%, and 6.63% in the RMSE, respectively. This is due to the ARIMA and SVR having difficulties in dealing with complex non-linear cellular traffic changes. Specifically, the ARIMA is a classical time-series analysis and prediction algorithm, which has a good effect on simple linear trend and periodic \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sampling Interval** & \multicolumn{3}{c}{**10 min**} & \multicolumn{3}{c}{**30 min**} & \multicolumn{3}{c}{**60 min**} \\ \hline
**Model Metrics** & **MAE** & **RMSE** & **R\({}^{2}\)** & **MAE** & **RMSE** & **R\({}^{2}\)** & **MAE** & **RMSE** & **R\({}^{2}\)** \\ \hline ARIMA & 58.24 & 102.33 & 0.53 & 178.44 & 216.23 & 0.48 & 378.55 & 453.11 & 0.27 \\ SVR & 52.67 & 85.31 & 0.57 & 176.24 & 211.65 & 0.50 & 340.59 & 407.26 & 0.30 \\ GRU & 34.22 & 41.72 & 0.77 & 104.51 & 129.27 & 0.63 & 235.11 & 276.50 & 0.41 \\ T-GCN & 27.96 & 40.78 & 0.81 & 79.22 & 122.40 & 0.69 & 170.16 & 232.58 & 0.50 \\ STA-GCN & 26.58 & 38.71 & 0.84 & 75.58 & 115.43 & 0.66 & 157.96 & 211.12 & 0.58 \\ DBSTGNN-Att & 26.02 & 37.98 & 0.83 & 71.24 & 106.43 & 0.71 & 149.81 & 197.13 & 0.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Numerical comparisons of the prediction performance. Figure 6: Prediction performance display of cell No. 4259 for different sampling intervals, with each time point representing a sampling interval, for a total of 144 time points. (**a**) Short-term prediction: 10-min sampling interval; and (**b**) mid-term prediction: 30-min sampling interval. change data.