Abstract: Network traffic has the characteristics of real-time, instability and correlation. The traditional prediction model of network traffic has the shortcomings of weak generalization ability and low prediction accuracy. To overcome these shortcomings, a network traffic prediction model (SCSO-GRU) based on GRU neural network combined with Sine-Cosine Swarm Optimization (SCSO) algorithm is proposed. Firstly, this paper introduces the particle update principle of SCSO algorithm. Then, it constructs a network traffic prediction model with SCSO-GRU neural network. The SCSO algorithm is used in model training to improve the training effect and overcome the disadvantage that the traditional GRU neural network converges to local optimum. Finally, this paper uses the SCSO-GRU model to predict the network traffic. The experimental results show that compared with the traditional LSTM and GRU models, the proposed model has better convergence efficiency and prediction accuracy, and can better describe the trend of network traffic.

Key words: network traffic prediction, SCSO algorithm, GRU neural network