Find in Library

Bandwidth is the core network resource. How to predict future traffic and adjust the network resource relocation early is an urgent problem to be solved. To this problem, a multi-time granularity GRU-BP neural network is proposed in this study for network traffic prediction. In the method, the network traffic data is fitted firstly by the cubic spline curve, and the fitted data is extracted according to different time granularities. Then, several GRU neural networks corresponding to specific time granularities are used for network traffic pre-prediction. Finally, the pre-prediction results of all the GRU neural networks are fed into the fully connected neural network. The fully connected neural network outputs the final network traffic prediction results. Comparison results show that the proposed method can improve the calculation accuracy by 9.0 % and reduce the calculation time by 28.6 %.