## 6 Experimental Setup and Results
### Overall Performance
Our MSCR framework, incorporating LSTM and diverse data sources, outperforms other models in predicting network traffic. Moreover, to advance the field of intelligent information systems, we have developed two distinct models: the transformer and the CNN-RNN (fusion). Both models represent our innovative approach to data integration and prediction tasks. Below, we present a comparative analysis of their performance. The transformer model is designed for high precision in scenarios with well-defined patterns, while the all-fusion model is tailored to deliver consistent accuracy across diverse and complex datasets. Figure 8 offers a detailed visual representation of the forecasting accuracy distribution for each method. This focuses on the expansive 27 \(\times\) 27 grid area and displays the accuracy distribution for the minimum, average, and maximum tasks. Figure 8: \(Cont\). In the context of the task minimum, both CNN-RNN and CNN-RNN (fusion) outshine other methods with accuracy levels ranging from 70% to 90%. This performance could be due to the inclusion of LSTM, which effectively captures temporal features. Notably, around 550 of the total 750 grids attain a prediction accuracy exceeding 70%. Regarding the task average, the top three prediction methods CNN-RNN, CNN-RNN(fusion), and T-DenseNet achieve accuracy in the 70% to 90% range. Interestingly, ARIMA also fares well within this range due to its lower variance at the task average. For the task maximum, which is substantially relevant for traffic offloading applications, CNN-RNN (fusion), T-DenseNet, and CNN-RNN demonstrate superior performance, with accuracy ranging between 70% and 90%. Nonetheless, it is worth highlighting that T-DenseNet exhibits m accurate grids within the 80% to 90% and 60% to 70% accuracy ranges. Our results demonstrate that both models significantly outperform existing methods, thus providing practical tools for optimizing B5G network management. This study seeks to answer how these advanced models can predict and manage network traffic more effectively in B5G networks. Also, the original CNN-RNN model displays promising results when addressing the network traffic problem across different tasks. Moreover, integrating diverse data types enhances the prediction accuracy across various grids. As illustrated in Figure 8, the accuracy rate is mainly within the 60% to 80% range. To delve deeper into the accuracy distribution, we employed the cumulative distribution function (CDF) depicted in Figure 9.  
Figure 8: Performance comparison of different methods for network traffic prediction. (**a**) Task Max: accuracy distribution, (**b**) Task Avg: accuracy distribution (**c**) Task Min: accuracy distribution. Figure 9: _Cont._  
Regarding the maximum task, our approach (CNN-RNN (fusion)), as noted in Table 9, achieves an accuracy of 81.7%, marginally higher than the T-DenseNet model. However, Figure 9a reveals that the T-DenseNet model's cumulative accuracy surpasses 70% in a more significant number of grids. This implies that the T-DenseNet model has more grids with high or low accuracies. In contrast, our method demonstrates a more consistent performance, yielding an average accuracy within the 60% to 80% range. Our method outperforms other techniques for average and minimum tasks, as reflected in Table 9. The CDF chart shows a more substantial accumulation of grids with over an 80% accuracy. Most of our method's grids achieve an accuracy above 70%, and the average accuracy significantly surpasses other methods. In conclusion, our method exhibits a higher average accuracy for each grid, especially in the average and minimum tasks. The CDF analysis shows that our method accumulates more grids with an accuracy above 80%. As evident in Figure 9, both the ARIMA and T-DenseNet methods tend to accumulate more grids with high and low accuracy, signaling a broad distribution of prediction performance.