Proceedings of the 2nd International Conference on Electronic & Mechanical Engineering and Information Technology (EMEIT 2012)

Application of Nonlinear Combination Prediction Model for Network Traffic

Authors
Aitao Zhao, Yingchun Liu
Corresponding Author
Aitao Zhao
Available Online September 2012.
DOI
10.2991/emeit.2012.519How to use a DOI?
Keywords
Network traffic, Support vector machine (SVM) , Prediction model, Combination
Abstract

To improve the network traffic prediction result, the paper put forward a nonlinear combination prediction model of network traffic flow. First, the single models of ARIMA, ARMA, GM (1, 1) were used in the prediction of characteristics of network traffic, Then three predicting results were input to a support vector machine for data fusion to obtain the final forecasting result The simulation results show that, compared with the other network traffic prediction models, the nonlinear combination forecasting model can well reflect the complex changes in network traffic, thereby improves the prediction accuracy of network traffic..

Copyright
© 2012, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2nd International Conference on Electronic & Mechanical Engineering and Information Technology (EMEIT 2012)
Series
Advances in Intelligent Systems Research
Publication Date
September 2012
ISBN
978-90-78677-60-4
ISSN
1951-6851
DOI
10.2991/emeit.2012.519How to use a DOI?
Copyright
© 2012, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Aitao Zhao
AU  - Yingchun Liu
PY  - 2012/09
DA  - 2012/09
TI  - Application of Nonlinear Combination Prediction Model for Network Traffic
BT  - Proceedings of the 2nd International Conference on Electronic & Mechanical Engineering and Information Technology (EMEIT 2012)
PB  - Atlantis Press
SP  - 2337
EP  - 2340
SN  - 1951-6851
UR  - https://doi.org/10.2991/emeit.2012.519
DO  - 10.2991/emeit.2012.519
ID  - Zhao2012/09
ER  -