# Traffic Prediction of Space-Integrated Ground Information Network Using the GTCN Algorithm
## 5 Simulation and Result Analysis
### Experimental Results and Discussion  
Due to its simple structure and high accuracy, the ARIMA model is chosen as a traditional prediction method, which is widely used in time series prediction. The ARIMA model is suitable for comparing and evaluating the advantages and disadvantages of the proposed algorithm. Additionally, recently, the RNN and its variation performed the network traffic prediction task very well, especially the gating GRU helped with LSTM neural network structure that makes the RNN free from the explosion gradient. Because LSTM and GRU have different performances in different tasks, in this paper, GRU and LSTM are selected as the GTCN to compare with the traditional cyclic neural network. As an improved algorithm, we included the TCN model in the experiment as well. In this experiment, the ARIMA model, GRU model, LSTM model, TCN model, and the proposed algorithm were implemented in the same simulation environment. After traffic prediction, the training time, the calculation time, and the root-mean-square error (RMSE) of the models were calculated as simulation evaluation indexes to objectively evaluate the proposed algorithm. The data from one-week duration were used as the training data and the data from the next day were used as the prediction samples for prediction and validation. Figure 8 shows the last 150 comparative results of the prediction results of the five models in this experiment. In the comparison results, the neural network used the Adam algorithm as the optimization algorithm. The gradient descent process has certain randomness; thus, to eliminate the effects of the randomness of each algorithm, in this experiment, the algorithms were executed ten times and the average result is presented as the final prediction results. Figure 8: Network Traffic Prediction Results of Each Algorithm. Except for the ARIMA model, which is an offline training method without the training time calculation, all the other models are compared with the results of 5000 times of training. The results show that the GRU model and the LSTM model could not converge rapidly under shorter training times. However, since the training frequency is too little, even though they could fit the trend, they do not perform well when dealing with the sudden burst of network traffic. The ARIMA model was able to achieve a better prediction after parameter adjustment. However, due to the need to re-analyze and adjust the parameters when processing different network traffic sequences, the model does not retain generalization. The TCN algorithm, due to the calculation without the gating control unit, the training speed and prediction speed were slightly faster; however, because the flow sequence sample size is large, the accuracy becomes low and the prediction is delayed. Since the integration of the Space-Integrated Ground Information Intelligent Network of the intelligent network has traffic bursts, this phenomenon shall bring a higher error. According to Figure 8, the GTCN algorithm proposed in this paper can achieve convergence rapidly with less training time, almost completely fit with the real value, and the prediction result is better compared to the other four algorithms. Table 1 shows the comparison of the experiment results. In this table, the prediction accuracy of the five algorithms and the comparison between the training speed and the running speed can be observed in detail. From the table, we can observe that under the same experiment environment and training times, the integrated intelligent network traffic prediction task of the Space-Integrated Ground Information Intelligent Network using LSTM with GRU, because of the need for data iteration, they have to wait for the current input to be evaluated to the next step of the calculation; thus their training times are longer, 811.57 s and 501.33 s, respectively.