# Traffic Prediction of Space-Integrated Ground Information Network Using the GTCN Algorithm
## 2 Related Work  
Currently, several forecasting models for ground and satellite network traffic have been proposed, such as the traditional ARIMA, Markov Model, gray prediction model, and cross-related entropy prediction model [7; 8]. The popular models based on machine learning, such as Recurrent Neural Network (RNN) and its variants, Long Short-Term Memory (LSTM) and Gated Recursive Unit (GRU), have taken advantage of their respective characteristics in various network traffic forecasting tasks. The traditional network traffic prediction models with offline training save memory and time and have achieved high precision. However, with the advancements of science and technology, especially the neural network algorithm, by using better CPU, GPU, and real-time training, machine learning algorithms for network traffic prediction models producing high accuracy and robustness have emerged. The author(s) in [9] proposed a network traffic prediction scheme based on the adaptive kernel least-mean-square (KLMS) algorithm, which predicted the satellite network traffic by minimizing the mean square error (MSE) through iterative weight coefficient. However, due to over-reliance on the training data, the algorithm was prone to the over-fitting phenomenon. In [10], the author(s) proposed an algorithm based on wavelet analysis and LSTM used in the Software Defined Network (SDN) for network traffic prediction. In this algorithm, the network traffic data are analyzed using wavelet decomposition denoising, which changes the modal sequences of the single-mode state to join the LSTM again after following the calculation process described above to increase the prediction accuracy. However, the field of view of this algorithm is limited, in which each prediction only perceives the estimated value and the detailed value of the previous node. As mentioned in the literature [11], the satellite network traffic has self-similarity property. Therefore, in the satellite network traffic with self-similarity, the global characteristics should be included in the calculation instead of simply referring to the data influence of the previous node in the traffic prediction of the intelligent network of the Space-Integrated Ground Information Intelligent Network. The author(s) in the literature [12] also uses the LSTM algorithm in the distribution of induction training to complete the Web traffic time series forecasting task, which results in greatly improved LSTM training speed. However, in the Space-Integrated Ground Information Intelligent Network, because of its sudden burst and self-similarity, the distributed asynchronous training might fail to grasp the flow characteristics, and the prediction accuracy of sudden traffic shall be greatly reduced. In the literature [13], the author(s) used Support Vector Regression (SVR) to perform phase-space reconstruction using real-time local sequence to determine the best SVR parameters for embedding dimension and delay time. However, in real-time, new data are included into the model calculation, thus all the parameters involved in the model change and constantly updated, making the algorithm complexity too high and not applicable for the Space-Integrated Ground Information Intelligent Network. The author(s) in the literature [14] implemented GPR (Gaussian Process Regression) to minimize the error propagation using step-wise prediction, as the traffic behavior of high-speed links depends on protocols (e.g., TCP, UDP, HTTP) at small time scales. However, the stepwise algorithm failed to consider the connection between local and global time series, so it cannot cope with the sudden bursts of the Space-Integrated Ground Information Intelligent Network. In the literature [15], the author(s) proposed the application of wavelet decomposition and reconstruction of the satellite communication network traffic data processing by using the Echo State Network (ESN) structure to create the model to predict network traffic data.