# Traffic Prediction of Space-Integrated Ground Information Network Using the GTCN Algorithm
## 5 Simulation and Result Analysis
### Experimental Results and Discussion
Moreover, due to the small receptive field during calculation, the algorithm cannot capture the characteristics of high volatility and high bursts of network traffic, leading to high RMSE values, which are 975.55 and 964.20, respectively. After data analysis and model screening, the ARIMA model can complete the prediction task better. ARIMA model has no training time because it uses offline training, so the calculation time of the prediction is slightly longer than the other three algorithms, which is 14.83 s. As a result, the ARIMA model has the highest prediction delay lag for the integration of the Space-Integrated Ground Information Intelligent Network of the intelligent network traffic burst. After capturing the periodicity of the network traffic, its RMSE value is also lower at 515.40, since in processing different time series, it must confirm the parameters needs artificially, leading to a weak generalization of the model. The TCN algorithm can process data rapidly through convolution. Compared with other algorithms, its training time is faster (44.62 s), and the prediction speed is the shortest (1.28 s). Compared with the traditional algorithms, its RMSE is lower (236.28). Using the value of RMSE as the basis for accuracy judgment, the proposed GTCN algorithm improved the prediction accuracy, training speed, and prediction speed, compared with the traditional three algorithms. The prediction accuracy was at maximum increased by 90.00%, the training speed was increased by at least 84.05%, and the prediction speed increased by at least 71.92%. Compared to the TCN algorithm, the GTCN algorithm has a higher training time and run time to improve the prediction accuracy, thus the accuracy of GTCN improved by 58.72% than the TCN algorithm. The GTCN can handle the problem of high burst, high volatility, and large sample size of the \begin{table}
\begin{tabular}{c c c c} \hline _Model_ & _RMSE_ & _Training Time(s)_ & _Running Time(s)_ \\ \hline LSTM & 975.55 & 811.57 & 7.23 \\ GRU & 964.20 & 501.33 & 7.41 \\ ARIMA & 515.40 & - & 14.83 \\ TCN & 236.28 & 44.62 & 1.28 \\ GTCN & 97.53 & 79.95 & 2.03 \\ \hline \end{tabular}
\end{table} 
Table 1: Experiment Results of the Comparison of the Different Algorithms. intelligent network of the Space-Integrated Ground Information Intelligent Network better and can fulfill the demand of its network traffic prediction task. We used the \"psutil\" module in the Python library to access the model memory value and use the maximum and average values to obtain the Maximum Instantaneous Memory Usage (MIMU) and Average Memory Usage (AMU). Table 2 shows the MIMU and AMU of each algorithm for training and prediction. The cost of improving the prediction accuracy of the algorithm in this paper can be analyzed in detail. According to the data in the table, since the GTCN algorithm proposed in this paper introduces TCN and the gating unit at the same time, the MIMU of the GTCN algorithm during training is higher than other comparison algorithms (90.01 MB), but the AMU of the GTCN algorithm is lower than the comparison algorithm (41.31 MB/s). Because the field of view of GTCN is larger than that of LSTM, GRU, and ARIMA, the MIMU and AMU of GTCN are lower than the above three algorithms, while the MIMU and AMU of GRU are slightly higher than the of the TCN algorithm due to the introduction of the gate control unit.