# Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network
## 5 Results Simulation and Analysis
### Comparison and Analysis of Simulation Results of Different Algorithms  
This experiment shows the prediction performance of AT-GRU by comparing it with the FARIMA model, the SVM model, and the GRU model.  
FARIMA: Fractional Difference Autoregressive Moving Average Model [28], which is a self-similar model, can capture both long-term and short-term correlation characteristics of traffic data.  
SVM (Support Vector Machine): It maps the feature vectors of instances to some points in the space [29]. The purpose of SVM is to draw a line to distinguish these two types of points, so that if there are new points in the future, this line can also make a good classification.  
GRU: Gate Recurrent Unit [30], which is a variant network form of RNN neural network, can effectively capture the characteristics between long sequences and alleviate the phenomenon of gradient disappearance or gradient explosion.  
The above experiments show the comparison results of the prediction effects of various prediction models. In order to clearly express the experimental results, in Figure 10, (b), (d), (f) and (h) are partial enlarged images of (a), (c), (e) and (g) respectively. Simulation experiments show that all the above four prediction models have a certain prediction ability, but the AT-GRU algorithm proposed in this paper has the most obvious fitting effect. The AT-GRU network model can better reflect the complex characteristics of satellite network traffic data, and its prediction performance is better. Compared with other prediction models, the error is lower, with an MAE of 14.24, RMAE of 20.37, and \(R^{2}\) score closest to 1. The FAR algorithm has the worst prediction performance, and the FARIMA traffic prediction model can only roughly predict the changing trend of traffic, but there is still a large error with the real traffic value. As FARIMA is a linear series model, it can only process short-time data series, but cannot fit complex nonlinear data, so the prediction error of this model is the largest. MAE was 33.73, RMAE was 42.20, and \(R^{2}\) scored the lowest. The SVM algorithm can fit the real value well, but the overall traffic prediction effect has some errors. Because the SVR algorithm is mainly used to solve the linear regression problem of small samples, it cannot well reflect the complex characteristics of satellite network traffic. For the prediction of large samples, the error is large, and the model MAE is 22.68 and RMAE is 32.25. In this paper, the attention mechanism is introduced into the GRU network. Compared with the traditional GRU prediction, it can make full use of the target sequence, and the prediction effect is better than that of a single GRU neural network model. The prediction error MAE of a single GRU model is 19.49 and RMAE is 28.08.  
As can be seen from Figure 11, AT-GRU has the best fitting effect and the lowest prediction error. The addition of an attention mechanism has promoted the prediction of GRU networks, which can improve the prediction accuracy of satellite network traffic to a certain extent and better meet the demand for satellite network traffic prediction.