# Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network
## 4 Experimental Results and Analysis
### Experiment Analysis
Deformable convolution and time embedding modules also had varying degrees of performance improvement for individual datasets. Overall, the performance improvement created by the attention mechanism was greater than the other two improvements. Embedding different modules had different effects on the model's runtime and parameters. Table 2 shows the changes in the time to train an epoch and the number of parameters under different conditions. We added each module individually to observe the results. Although the time embedding module and the attention module had added parameters, they had little effect on the running time. The addition of deformable convolution reduced the parameters by about 69K, but complex calculations increased the running time by nearly half. There was no difference in the running cost of the three datasets due to the same structure. Considering the performance and cost of HSTNet, we think that the time embedding module and attention module were the most effective strategies for improving model performance. Deformable convolution could improve prediction performance to a certain extent, but it also increased the running time of the model. It should be mentioned that DenseBlock with more filters could obtain better performance, but the training time was greatly increased. For example, if we replaced the original 32 filters with the 64 filters, we could increase the above results of RMSE by 2.3%, but nearly three times the training time in SMS. In Section 3.1, we mentioned that the input data included the last three time periods and the current time period of the previous three days. We input different time dimensions to analyze the impact on HSTNet's performance. The \(N\)-dimensional data represented the data from the last \(N\) time periods and the current time period of the previous \(N\) days. As shown in Table 3, different time dimensions had a certain impact on the RMSE results. In the three datasets, HSTNet achieved the best performance when inputting three-dimensional data. The performance was slightly poorer when only one- or two-dimensional data were input, which indicated that the model could not extract the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Time** & **Parameters** \\ \hline STDenseNet & 22s & 239K \\ +DeformConv & 34s & 170K \\ +Time-property & 23s & 350K \\ +Attention & 22s & 243K \\ HSTNet & 35s & 284K \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The effects on parameters and the time of each epoch. Figure 9: Comparison of different module effects based on STDenseNet. spatiotemporal characteristics of the traffic well in this case. When \(N=4\), the performance of the model would also decrease. We believe that this was caused by the introduction of more low correlation data. Figure 10 shows the cell of the Internet dataset (55,58), the predicted values of the five methods compared to the real values. To visually show the difference in performance, we compared their accumulation error in the lower right subgraph of Figure 10. The predicted values of HA and ARIMA had large errors with the true values, and they lacked accuracy in fitting the peaks. The method based on deep learning was much better than the traditional method. HSTNet's overall fitting effect on traffic was higher than the other three. Especially during peak hours of network traffic, HSTNet achieved more accurate predictions. Compared to STDenseNet, its overall error in this area decreased by nearly 10% because HSTNet had better spatiotemporal feature extraction capabilities.