# Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network
## 2 Literature Review  
In recent years, satellite network traffic forecasting methods have emerged endlessly, and the forecasting accuracy and efficiency of the different forecasting methods are different. The prediction models can be roughly divided into traditional mathematical-statistical fitting models and popular prediction models based on machine learning neural networks. Markov model and time series model are commonly used in mathematical fitting models. [PERSON] et al. analyzed the disadvantages of the usual Poisson traffic model and proposed a deterministic Markov modulation process model [3] to simulate satellite network traffic. The process of traffic acquisition, storage, and transmission was transformed into a queuing model, some closed expressions of service quality indicators were derived, and the validity of the theory was verified. [PERSON] et al. put forward the Autoregressive Moving Average Model (ARMA) to forecast satellite network traffic [4], and the current traffic sampling value is represented by the weighted sum of several historical traffic sampling values. [PERSON] et al. introduced the geographic longitude of the satellite and the transit time of traffic to establish a mathematical model and proposed a forecasting algorithm based on the proxy model to forecast the traffic volume in the satellite coverage area [5]. Although FARIMAcan obtain the long-term and short-term correlation characteristics of network traffic data itself, the model is highly complex and its prediction accuracy is not high [6]. Traditional mathematical-statistical fitting models have poor prediction accuracy for current network traffic and lack good description ability for satellite long-term correlation characteristics, so they cannot be well used in satellite network traffic prediction. In recent years, with the rapid development of artificial intelligence, neural networks are widely being used in satellite network traffic forecasting. In recent years, many experts and scholars have been using neural networks and deep learning to predict satellite network traffic. Compared with the traditional mathematical-statistical fitting model, the neural network model has a strong nonlinear mapping ability, generalization ability for complex network systems, and strong self-learning and self-organizing ability [7]. [PERSON] et al. proposed a new model (SSA-AWELM) [8] which combines singular spectrum analysis (SSA) with AdaBoost weighted extreme learning machine (AWELM). SSA was developed to decompose the original data into three components: trend, periodicity, and residue. AWELM was developed to seperately predict every component. The three prediction results are added up to the final result. [PERSON] et al. proposed combining Recurrent Neural Network (RNN) and GRU into a new network model [9] and applying it to network traffic prediction. A large number of experimental results show that the prediction result of this model is close to the real value. [PERSON] et al. proposed a nonlinear GCN-GAN network model [10]. This model combines GCN and GAN, extracts the complex characteristics of network traffic through Graph Convolutional Neural Network (GCN), and uses Generative Adversarial Networks (GAN) to model the data structure. The proposed model realizes the prediction of burst traffic. [PERSON] et al. put forward a new prediction model, LA-ResNet [11]. The innovation of this model is that an attention mechanism is added, but the disadvantage of this model is its complicated structure. Network traffic data is a kind of complex data with multiple characteristics, and the prediction accuracy of common models is not high. For neural networks, the more super-parameters there are, the larger the network will be and the more complex the model. To improve the prediction accuracy of the model and reduce the complexity of the model, [PERSON] et al. proposed a traffic prediction strategy to improve the LSTM network with a genetic algorithm [12].