## 6 Experimental Setup and Results
### Benchmarking with TE-CNN-RNN  
Our experiment used advanced hardware and software for reliable performance benchmarks. We used an Intel(r) Core(tm) i7-14700 CPU with 28 threads, an NVIDIA GeForce GTX 1080 Ti graphics card, and 31.1 GiB of RAM. The software environment was Ubuntu 20.04.6 LTS with a 64-bit OS type. We also tailored the loss function selection for each model, with the TE-CNN-RNN using the mean absolute error (MAE) and Huber loss according to the prediction context. This approach aimed to enhance the model's predictive accuracy across various scenarios. Table 2 summarizes the parameters optimized for each model, including those specific to our TE-CNN-RNN architecture. For TE-CNN-RNN, TSMixer and SAMformer, the following parameters were identified through Optuna,  
Table 3 presents a comprehensive performance comparison of our CNN-RNN (INT) baseline, TSMixer, SAMformer, and the proposed TE-CNN-RNN model, focusing on \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **CNN-RNN (INT)** & **TSMixer** & **SAMformer** & **TE-CNN-RNN** \\ \hline Learning Rate & 0.00002 & 0.009 & 0.009 & 0.009 \\ \hline Batch Size & 96 & 58 & 58 & 58 \\ \hline Optimizer & SGD & Adam & Adam & adagrad \\ \hline Clipnorm & - & 9.98 & 9.98 & 1.2 \\ \hline L2 norm & 0.001 & \(1\times 10^{-5}\) & \(1\times 10^{-5}\) & 0.0002 \\ \hline Embedding nodes & 100 & - & - & - \\ \hline \multicolumn{5}{c}{**Exclusive Parameters for TE-CNN-RNN**} \\ \hline Num Heads & 3 & Coefficient & 30 & CNN Filter & 153 \\ \hline Fusion FFN & 246 & Kernel Size & 5 & Time Steps & 5 \\ \hline D Model & & 90 & Dropout Rate & 0.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optimization parameters for CNN-RNN (INT), TSMixer, SAMformer, and TE-CNN-RNN models. their ability to forecast network traffic. The TE-CNN-RNN model is superior across all tasks (Max, Avg, Min), consistently achieving the lowest RMSE scores and highest MA percentages. This validates its accuracy and robustness for time-series analysis. TSMixer and SAMformer represent advanced time-series forecasting methods. TSMixer excels in blending operations across time and feature dimensions, while SAMformer enhances efficiency through sharpness-aware optimization and channel-wise attention. However, our TE-CNN-RNN's advantage likely stems from its comprehensive approach. The integration of CNNs, transformer blocks, and GRUs, combined with our fusion mechanism, enables the model to effectively capture complex spatial-temporal patterns and long-range dependencies inherent in network traffic data. Table 4 complements the findings from the figures, showing that the TE-CNN-RNN achieved the highest MA and the lowest RMSE consistently across all tasks, further confirming its superior performance. The confidence intervals included in the table reflect the robustness and reliability of these results over five runs with different random seeds. An analysis of variance (ANOVA) was utilized to assess the prediction accuracy among various time groups and model types, yielding F-statistics of 33.713 (\(p<0.001\)) for time groups and 51.838 (\(p<0.001\)) for models. These results indicate a highly significant discrepancy in performance. Notably, the TE-CNN-RNN model emerged as the most accurate, consistently outperforming TSMixer and SAMformer over multiple time intervals and across differing levels of internet traffic demand. The statistical tests support the robustness of the TE-CNN-RNN, demonstrating its predictive reliability that is not merely a consequence of sample variation. This tailored analysis method aligns with our goal of creating a forecasting model adept at navigating the complexities of real-world internet traffic characterized by dynamic temporal shifts and diverse volume changes. Furthermore, the enduring superiority of the TE-CNN-RNN across a range of prediction tasks affirms its utility and sets a precedent in network traffic forecasting endeavors. This model's performance establishes a new standard in the domain, highlighting the