## 6 Experimental Setup and Results  
### Experimental Settings  
We conducted a comparative study on mobile traffic prediction across various models using the Telecom Italia dataset for Milan City. The models, including ARIMA, ST-ResNet, T-DenseNet, and CNN-RNN [13; 19; 20], encompass deep learning and traditional forecasting techniques. For this study, we reformatted INT, Milano Today, Social Pulse, and periodic data into four-dimensional arrays as described in Section 5, with the array parameters \((T,H,V,F)\) set to \((6,15,15,1)\). This configuration spans a 15-by-15 grid area, covering 225 grids in the Milan Grid, each representing six temporal data points within one hour. The output array parameters were defined as \((T,H,V,F)=(1,3,3,1)\), focusing on predicting network traffic within a central \(3\times 3\) grid area from the original \(15\times 15\) grid, on emphasizing city center traffic forecasting. To reflect the broader city-wide traffic, we expanded the prediction scope to approximately a \(27\times 27\) grid area, covering the entire city center of Milan. Figure 6(a) displays the network traffic for this expanded area. Our objective was to predict network traffic over this extensive area. Different ranges for prediction were selected, as shown in Figure 7b, with the blue and yellow boxes indicating the chosen prediction ranges. The feature extraction was initially performed within the blue box area, followed by predictions within this range before proceeding to the yellow box area. This iterative process, repeated 81 times, covered the entire city center, culminating in an aggregated average to formulate the final prediction. In our deep learning model, we optimized its performance by adjusting several parameters. The following are the parameters we set for the model:  
* For the abbreviations of each data type, please refer to Table 1.
* CNN-RNN: The overall structure of the CNN-RNN model remains similar to our previous work [13]. * Table 2 summarizes the parameters optimized. * Training data: 90% of the data from all but the last two weeks for training, and 10% for validation
* Testing data: The second-to-last week. Table 1 shows the full names and abbreviations used for the different data types. To evaluate the performance of our model, we employed two commonly used metrics: the mean absolute percentage error (\(MAPE\)) and the root mean square error (\(RMSE\)). These metrics allow us to assess the accuracy of our predictions. Additionally, we calculated the mean accuracy (\(MA\)), which provides a more intuitive measure of accuracy between the real and predicted values. The \(MAPE\) and \(RMSE\) are defined as follows:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(|y_{i}-\hat{y}_{i}|)^{2}} \tag{10}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}}\right| \tag{11}\] \begin{table}
\begin{tabular}{l l} \hline \hline
**Abbreviation** & **Full Name** \\ \hline INT & Internet traffic \\ D & Daily features \\ W & Weekly features \\ E & External weather data \\ M & Milano Today \\ S & Social Pulse \\ all & Internet+D+W+E+MT+SP \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Full names and abbreviations. Figure 7: Grid heat map and selection feature. (**a**) \(27\times 27\) grid heat map, (**b**) Method of selecting features. The \(MA\) is calculated as:  
\[MA=(1-MAPE)\times 100 \tag{12}\]  
These metrics provide insights into the accuracy and performance of our deep learning model in predicting mobile traffic. To address the challenges of mobile internet traffic forecasting, we compare our proposed models against a diverse set of baselines. These baselines include statistical models, feature-focused deep learning methods, and recent transformer-based architectures. This multifaceted comparison will illuminate the strengths and potential shortcomings of our model, guiding future refinements in mobile internet traffic forecasting:  
* **ARIMA**: ARIMA is always used in statistics and econometrics, particularly in time series analysis.