Abstract: In order to improve the medium-term prediction accuracy of network traffic, a network traffic prediction method based on auto regressive integrated moving average (ARIMA) with Gaussian process regression compensation was proposed. Firstly, the linear and nonlinear characteristics of network traffic can be determined by Brock-Dechert-Scheinkman statistics. Then, ARIMA model was used for modeling the non-stationary network traffic time series. The linear model of network traffic sequence was obtained. The artificial bee colony algorithm optimized Gaussian process regression model was used as the prediction model of predictive error sequences with the nonlinear characteristic. Finally, the final prediction value was obtained by adding predictive values of ARIMA model and predictive error values of Gaussian process regression model. Simulation comparison shows that the proposed prediction method has higher prediction accuracy with the smaller prediction error.

Key words: network traffic, prediction, auto regressive integrated moving average, Gaussian process regression, Brock-Dechert Scheinkan statistics