关键词: 流量预测, 时空相关性, 奇异矩阵, 梯度提升回归树, 预测精度

Abstract:

In view of the current traffic prediction model does not consider the problem of temporal and spatial correlation, which affects the prediction accuracy, a prediction method based on satellite network traffic gradient boost regression tree is proposed. Firstly, the spatiotemporal correlation of the satellite network traffic is analyzed, and the spatiotemporal correlation traffic with the target satellite traffic is extracted as the original prediction input. Then, the singular matrix decomposition is carried out for the spatiotemporal correlation traffic to eliminate the information overlap and redundancy of the relevant input traffic. Finally, the improved gradient boost regression tree is used for prediction. Simulation results show that this method can efficiently improve the training speed of satellite network traffic prediction, and the prediction accuracy is slightly higher.

Key words: traffic prediction, spatiotemporal correlation, singular matrix, gradient boost regression tree, prediction accuracy