Traffic prediction using neural networks

Edmund S. Yu, C. Y.Roger Chen

Research output: Chapter in Book/Entry/PoemConference contribution

58 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationIEEE Global Telecommunications Conference
PublisherIEEE Computer Society
Number of pages5
ISBN (Print)0780309170
StatePublished - 1993
EventProceedings of the IEEE Global Telecommunications Conference. Part 2 (of 4) - Houston, TX, USA
Duration: Nov 29 1993Dec 2 1993

Publication series

NameIEEE Global Telecommunications Conference


OtherProceedings of the IEEE Global Telecommunications Conference. Part 2 (of 4)
CityHouston, TX, USA

ASJC Scopus subject areas

  • General Engineering


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