# Traffic Prediction of Space-Integrated Ground Information Network Using the GTCN Algorithm
## 1 Introduction  
The Space-Integrated Ground Information Network consists of space-based network backbone, space-based access network, networks foundation such as nodes, which are connected in a star topology to the land, the sea, and the air environment. It is a variety of communication platforms combining a new type of network architecture, flexible node deployment, and global coverage, to provide free Internet services for space-based network foundation and sea-based platform users with low latency, high reliability, and strong information and communication security infrastructure [1; 2; 3; 4; 5; 6]. With the wide application of the Space-Integrated Ground Information Network, the requirements for computing speed, storage space, and communication and forwarding resources allocation are increasing. As a result, a machine learning algorithm is introduced into the Space-Integrated Ground Information Network to optimize the network performance and the Space-Integrated Ground Information Network begins to evolve towards the Space-Integrated Ground Information Intelligent Network.  
The Space-Integrated Ground Information Intelligent Network is a new intelligent network architecture based on the Space-Integrated Ground Information Network, which uses a machine learning algorithm to optimize network resource allocation, improve network utilization, and implement efficient network information transmission, global space-timecontinuous communication, highly reliable, continuous communication, and other capabilities. Accurate network traffic prediction is critical for dynamic resource management in the Space-Integrated Ground Information Intelligent Network, and it requires queue scheduling and traffic classification. Network traffic prediction supports effective allocation of network resources, thus speeding up the transmission, avoiding congestion, and reducing the loss of user equipment and base station. However, the accurate prediction of traffic data of the Space-Integrated Ground Information Intelligent Network is challenging because of the highly dynamic network topology, scattered satellite nodes, and long link that lead to frequent interruptions which all result in the high sudden burst and high volatility of traffic in the Space-Integrated Ground Information Intelligent Network.  
In this paper, our main work is to search for the shortest time to efficiently analyze and train the simulated time series data of the Space-Integrated Ground Information Intelligent Network, to capture the characteristics of the network traffic, such as high bursts and high volatility, to predict the network traffic data immediately and accurately. First, we divided the data into a training set and a test set, defined the data standardization, and divided the data according to the autocorrelation of network traffic sequence for subsequent calculation by applying the GTCN algorithm. The GTCN algorithm is created based on the TCN that processes the block after the network traffic time series data and feature extraction. Then, store the feature into gating neurons to achieve memory saving, speed up the model processing, and improve the prediction accuracy.  
The rest of the paper is arranged as follows: in the following section, we presented several related works. In the third section, we introduce the structure of the TCN algorithm and in the fourth section, we perform data processing and modeling for the traffic characteristics of the Space-Integrated Ground Information Intelligent Network and propose the GTCN algorithm. In the fifth section, we use OPNET to simulate the traffic data of the Space-Integrated Ground Information Intelligent Network and the traffic data of the ground network to demonstrate the high volatility of the traffic of the Space-Integrated Ground Information Intelligent Network. Then, we applied a variety of algorithms to complete the network traffic prediction task and discuss the experimental results. Finally, we summarize the experimental results and discuss future research in the sixth section.