# Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network
## 4 Experimental Results and Analysis
### Experiment Analysis  
To compare the performance of HSTNet proposed in this paper, we selected three existing traffic prediction algorithms as the baseline of this experiment: historical average (HA), ARIMA, LSTM, and STDenseNet. Four methods performed MAE- and RMSE-based evaluations on three datasets. The result is shown in Figure 8.  
From Figure 8, we can see that HSTNet's MSE and RMSE performance in the three traffic datasets was ahead of the other existing algorithms. The historical average was simply calculated from historical data and lacked the mining of deep correlations. ARIMA only considered the historical timing characteristics of the data, without regard for other dependencies. The performance of LSTM was better than statistical methods, but worse than other deep learning methods. STDenseNet did not consider the impact of external factors. Our model not only better extracted the spatial correlation of traffic data, but also considered the impact of time attributes on traffic changes, so the best performance was achieved. In Table 1, we calculate the model evaluation results after adding the deformable convolution, time embedding module, and attention module in STDenseNet. In Table 1, +DeformConv represents embedding only deformable convolutions in the baseline. HSTNet was evaluated after incorporating three improvements. The model achieved the best MAE and RMSE performance on Call, and the effect on SMS was slightly worse. The effect of the model on Internet improved, but the overall performance was far worse than Call and SMS because the traffic on Internet was very different from the SMS and the Call traffic. In some cases, it was close to ten times the gap. Considerable traffic changes had a large impact on model performance. It is worth mentioning that the traffic gap between different cells after integration was about twice that of the original, which was more complicated than the separate prediction. Therefore, compared to the results in [17], our experiments obtained larger RMSE and MAE on SMS and Call. Figure 9 shows the performance improvement of the model by adding different modules and HSTNet. For the SMS datasets, the addition of deformable convolution, time embedding, and attention modules increased by 2.61%, 3.96%, and 9.09%, respectively, the RMSE. For the MAE, there were 2.11%, 0.98%, and 3.16% performance improvements. For the Call dataset, the addition of deformable convolution units had an increase in 6.39% and 5.38% in MAE and RMSE, respectively; the attention module had a 10.57% improvement in MAE. For the Internet dataset, the three improvements we \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Model** & **MAE** & **RMSE** \\ \hline \multirow{4}{*}{SMS} & STDenseNet & 11.10 & 27.49 \\  & +DeformConv & 10.81 & 26.91 \\  & +Time-property & 10.66 & 27.22 \\  & +Attention & 10.09 & 26.62 \\  & HSTNet & 10.01 & 26.42 \\ \hline \multirow{4}{*}{Call} & STDenseNet & 8.13 & 17.10 \\  & +DeformConv & 7.61 & 16.18 \\ \cline{1-1}  & +Time-property & 8.03 & 16.89 \\ \cline{1-1}  & +Attention & 7.27 & 16.70 \\ \cline{1-1}  & HSTNet & 7.25 & 16.04 \\ \hline \multirow{4}{*}{Internet} & STDenseNet & 44.15 & 80.51 \\  & +DeformConv & 43.23 & 77.75 \\ \cline{1-1}  & +Time-property & 39.73 & 77.08 \\ \cline{1-1}  & +Attention & 39.89 & 74.48 \\ \cline{1-1}  & HSTNet & 39.19 & 72.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall performance of the model. Figure 8: Comparison of prediction performance on the baseline and HSTNet. HA, historical average. proposed still improved the results to varying degrees. The effect of adding the time attribute and the attention module was very obvious and had an approximately 10% improvement on the MAE. It can be seen that the three different improvements improved the results in the different datasets, verifying the correctness of the hypotheses in Section 3. Different improvements had different effects on the three datasets. The attention module was very impressive for predicting the performance of all datasets.