# Traffic Prediction of Space-Integrated Ground Information Network Using the GTCN Algorithm
## 4 GTCN Algorithm
### GTCN for Traffic Prediction  
Since TCN is a variant of CNN and has expanded causal convolution to expand receptive field, it has most of the advantages of CNN, such as high parallelism, flexible receptive field, gradient stability, and lower memory consumption compared with RNN and its variant algorithms. The accuracy and speed of time series prediction are beyond doubt. Moreover, the field of view of TCN is much higher than RNN and its variants, makes TCN very suitable to capture the characteristics of high burst and high volatility of the Space-Integrated Ground Information Intelligent Network. However, in the TCN, the convolution process is limited by the size of the reception field; thus, in dealing with longer time series such as network traffic data, the reception field is restricted due to slow processing speed and memory consumed. The TCN neurons will ignore the characteristics between data in the time series, especially if the capture duration is longer than the time series, causing poor performance. This property of the GRU helped the door control unit store longer sequence information and characteristics to capture the high burst and high volatility of the network traffic. The long network traffic is based on the time sequence of the relevant section in TCN for training and introduction to put every trained sequence control neural cell update and reset, the input should be in sequence instead of the data points. To ensure complete capture of the sequence of each segment information, instead of recording the characteristics of the entire time series, TCN neurons only need to record the generalized characteristics of each segment and turn to the gating unit to record the relationship between the sequences. This algorithm can reduce resource consumption, provide better performance to the advantages of TCN, compensate for TCN due to the limited vision field to capture more web traffic sequence information, and because of its high parallelism, can undertake various sectional training at the same time, to improve data processing efficiency, more rapidly and accurately, and complete the integration of the Space-Integrated Ground Information Intelligent Network prediction tasks. The structure of GTCN is shown in Figure 6.  
When the GTCN model receives the network traffic sequence, it creates data segmentation according to the autocorrelation of the data. After the segmentation of network traffic data, we put the processed data into the TCN model, in which each segment is trained using the same TCN model. We adjust the dropout parameters of TCN and use a small number of TCN neurons in each training to ensure the generalization of the TCN neural network. Since the input and output lengths of TCN are the same, to avoid affecting the length of the segment, a part of the next segment is be added as the training sample, and the sample size of the next segment equal to the multiple of the sample size represented by the self-correlation degree. Then, the training results of this segment and the next segment are the input of the gating unit for training, and the gating unit is reset and updated to remember the eigenvalues of each data segment. In the prediction stage, we select the last segment as the input to obtain the prediction result. Compared with RNN and its variants, GTCN has a larger field of view, which can clearly capture the high burst and high volatility of the intelligent network traffic data of the Space-Integrated Ground Information Intelligent Network, and faster computing speed. Compared with the TCN algorithm, GTCN can record the data features of previous data using the gating unit to reduce the storage pressure of the TCN neurons, thus capturing the periodicity of the intelligent network traffic data of the Space-Integrated Ground Information Intelligent Network, leading to higher prediction accuracy.