Modeling and Prediction of Network Traffic Based on Hybrid Covariance Function Gaussian Regressive

摘要

In order to obtain better predict results of the network traffic, this paper proposes a novel network traffic prediction model based on hybrid covariance function Gauss Process (GP). Firstly, GP model is built by using hybrid covariance function, and then the network training set is input to GP model for training to find the optimal parameter of covariance and mean function, finally, network traffic prediction model is established, and one-step and multi-step network traffic prediction test are carried out to test the performance compared with support vector machine, the neural network, and the traditional Gauss process. The results show that, compared with the contrast model, the proposed mode can describe the change trends of network traffic, and improve the prediction accuracy of network traffic, so it is an effective prediction method for complex network traffic.