Traffic prediction using neural networks

Broadband ISDN has made possible a variety of new multimedia services, but also created new problems for congestion control, due to the bursty nature of traffic sources. Traffic prediction has been shown to be able to alleviate this problem in [1, 2]. The traffic prediction model in their framework is a special case of the Box-Jenkins' ARIMA models. In this paper we would like to go one step further and propose a new approach, the neural network approach, for traffic prediction. A (1, 5, 1) back-propagation feedforward neural network is trained to capture the linear and nonlinear regularities in several time series. A comparison between the results from the neural network approach and the Box-Jenkins approach is also provided. The non-linearity used in this paper is chaotic. We have designed a set of experiments to show that neural networks' prediction performance is only slightly affected by the intensity of the stochastic component (noise) in a time series. We have also demonstrated that a neural network's performance should be measured against the variance of the noise to gain more insight into its behavior and prediction performance. Based on the experimental results we then conclude that the neural network approach is an attractive alternative to traditional regression techniques as a tool for traffic prediction.