Traffic prediction using neural networks

Abstract

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 W i c sources. Traflic @ction has been shown to be able to alleviate this problem in [l, 21. The M i c 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 trait prediction. A (1, 5, 1) back-propagation feedforward neural network is trained to capture the linear and nonlinear regulanties in several time serieses. A comparison between the results 6om 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 W c t i o n 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 trafic prediction. L IntroductionThe advent of broadband integrated services digital networks (l3-ISDN) has made possible a variety of new multimedia services, but it has also created new problems for congestion control. Most of the problems arise because of the bursty nature of the traEic sources. It has been argued that traditional reactive congestion control is not suitable for broadband integrated networks due to the effects of high-speed channels[3, 41. Recently, Lazar et al. have proposed a new congestion control scheme, called proactive control, to Overcome this difficulty [l, 21. The core of their proactive congestion control scheme lies in the trafiic predictor. The traffic predictor in their framework is a seasonal autoregressive (AR) model, which is a special case of the BoxJenkins' auto-regressive integrated moving average (ARLMA) models.Among traditional regression techniques the ARIh4A models are said to be optimal forecasts [5]. They are optimal in the sense that no other univariate forecasts have a smaller mean squared error (MSE). However, this comparison is valid only for those univariate models that are linear combinations of the past values in the time series, with fixed coefficients. The seasonal AR model used in [ 1,2] likewise can only capture the linear relationship among the cell arrivals of t r s c sources. Non-linear regression techniques do exist, but they require much more computational and intellectual efforts. This fact 0-7803-0917-0/93$03.~ Q 1993 IEEE severely limits their practicality. In this paper we would like to propose a new approach, the neural network approach, for traflic prediction. It is well known that neural networks are capable of performing non-linear mappings between realvalued i...

A Collision Alleviation Scheme for IEEE 802.11p VANETs

“…As far as prediction algorithm is concerned, many research results could be applied to approximately estimate the number of active stations in the wireless networks [16][17][18][19][20]. However, in addition to the difficulties in acquiring sufficient knowledge of the system, these type of approximations tend to be very computationally complex, and subject to significant errors, especially in high traffic load situations.…”

Traffic predictor using neural network and its performance

“…The neural network can create the desired multivariable function by learning from case studies. Methods have been proposed in which a neural network is applied to path selection control or call acceptance control [3,4]. In this paper, the neural network is simply used as a traffic predictor, and performance is analyzed by comparison with the linear prediction method.…”

The limitations of artificial neural networks for traffic prediction

“…Early work on traffic prediction [4][2] [6] involved the prediction of traffic generated using a traffic model, as such traffic models have been shown to posses similar properties to real traffic.…”

“…A similar scheme is proposed in [2], in which a neural network prediction scheme is used to dynamically allocate bandwidth to traffic streams, however the same linear traffic model is used. Yu and Chen [6] compare the performance of this scheme with a prediction scheme based on a linear regression fit of the training data, and conclude that the performance of both schemes is very similar.…”