Traffic Modeling and Prediction using ARIMA/GARCH Model

The predictability of network traffic is a significant interest in many domains such as congestion control, admission control, and network management. An accurate traffic prediction model should have the ability to capture prominent traffic characteristics, such as long-range dependence (LRD) and self-similarity in the large time scale, multifractal in small time scale. In this paper we propose a new network traffic prediction model based on nonlinear time series ARIMA/GARCH. This model combines linear time series ARIMA model with non-linear GARCH model. We provide a parameters estimation procedure for our proposed ARIMA/GARCH model. We then evaluate a scheme for our models’ prediction. We show that our model can capture prominent traffic characteristics, not only in large time scale but also in small time scale. Compare with existing FARIMA model, our model have better prediction accuracy.