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 traffic sources. Lazar and Pacifici (1991) showed that traffic prediction is able to alleviate this problem. The traffic prediction model in their framework is a special case of the Box-Jenkins ARIMA model. In this paper, we propose a neural network approach for traffic prediction. A (1,5,1) backpropagation 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 nonlinearity used in this paper is chaotic. We have designed a set of experiments to show that a neural network's 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, in order to gain more insight into its behavior and prediction performance. Based on experimental results, we then conclude that the neural network approach is an attractive alternative to traditional regression techniques as a tool for traffic prediction.>
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Yu et al. were the first to apply ML in traffic prediction using MLP-NN.
“To the best of our knowledge, Yu et al. [489] were the first to apply ML in traffic prediction using MLP-NN.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [489]
Cites
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892 · see citing works
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728 · see citing works
Book Review: Time Series Analysis: Forecasting and Control
Geurts, M. D., Box, G. E. P., & Jenkins, G. M. (1977) · Journal of Marketing Research · doi:10.2307/3150485 · Cited by 19,399 · see citing works
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883 · see citing works
Multilayer feedforward networks are universal approximators
HornikK., StinchcombeM., & WhiteH. (1989) · Neural Networks · doi:10.5555/70405.70408 · Cited by 9,266 · see citing works
Time Series Analysis: Forecasting and Control
Kleiner, B., Box, G. E. P., & Jenkins, G. M. (1977) · Technometrics · doi:10.2307/1267710 · Cited by 6,447 · see citing works
On the approximate realization of continuous mappings by neural networks
Funahashi, K.-I. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90003-8 · Cited by 4,206 · see citing works
Predicting chaotic time series
Farmer, J. D., & Sidorowich, J. J. (1987) · Physical Review Letters · doi:10.1103/physrevlett.59.845 · Cited by 1,951 · see citing works
Performance models of statistical multiplexing in packet video communications
Maglaris, V., Anastassiou, D., Sen, P., et al. (1988) · IEEE Transactions on Communications · doi:10.1109/26.2812 · Cited by 983 · see citing works
Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings
White, H. (1990) · Neural Networks · doi:10.1016/0893-6080(90)90004-5 · Cited by 683 · see citing works
Survey of traffic control schemes and protocols in ATM networks
Bae, J. J., & Suda, T. (1991) · Proceedings of the IEEE · doi:10.1109/5.64405 · Cited by 289 · see citing works
A congestion control framework for high-speed integrated packetized transport
Woodruff, G., Rogers, R. G. H., & Richards, P. (2003) · doi:10.1109/glocom.1988.25837 · Cited by 152 · see citing works
Control of resources in broadband networks with quality of service guarantees
Lazar, A. A., & Pacifici, G. (1991) · IEEE Communications Magazine · doi:10.1109/35.99263 · Cited by 99 · see citing works
Predicting the Future: Advantages of Semilocal Units
Hartman, E., & Keeler, J. D. (1991) · Neural Computation · doi:10.1162/neco.1991.3.4.566 · Cited by 73 · see citing works
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Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036 · see citing works
Predicting future traffic using Hidden Markov Models
Chen, Z., Wen, J., & Geng, Y. (2016) · 2016 IEEE 24th International Conference on Network Protocols (ICNP) · doi:10.1109/icnp.2016.7785328 · Cited by 72 · see citing works
- Authors
- Yu, E. S., & Chen, C. Y. R.
- Affiliations
- Syracuse University (US)
- ORCID
- Yu, E.S.
- Venue
- Proceedings of GLOBECOM '93. IEEE Global Telecommunications Conference
- Publisher
- IEEE
- Published
- 2002-12-30
- Pages
- 991-995
- ISSN
- 1930-529X (ISSN-L)
- OpenAlex
- W2114975872
- Cited by
- 77
- References
- 14
- Citations by year
- 2026: 3 · 2025: 3 · 2023: 4 · 2022: 3 · 2021: 8 · 2020: 7 · 2019: 2 · 2018: 5 · 2017: 4 · 2016: 4 · 2015: 1 · 2012: 1
- FWCI
- 3.53
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Globecom (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Keywords
- traffic prediction, broadband ISDN, congestion control, bursty traffic, BP neural network, ARIMA comparison, chaotic time series, noise intensity, prediction performance evaluation, multimedia traffic
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