Multi-scale network traffic prediction using K-factor gegenbauer ARMA and MLP models

The 3rd ACS/IEEE International Conference onComputer Systems and Applications, 2005. ◽

10.1109/aiccsa.2005.1387062 ◽

2005 ◽

Author(s):

N. Sadek ◽

A. Khotanzad

Keyword(s):

Network Traffic ◽

Traffic Prediction ◽

Multi Scale ◽

K Factor ◽

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Multi-scale high-speed network traffic prediction using k-factor Gegenbauer ARMA model

2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577) ◽

10.1109/icc.2004.1312898 ◽

2004 ◽

Author(s):

N. Sadek ◽

A. Khotanzad

Keyword(s):

Network Traffic ◽

Traffic Prediction ◽

Multi Scale ◽

High Speed Network ◽

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Multi-Scale Network Traffic Prediction Using a Two-Stage Neural Network Combined Model

2006 International Conference on Wireless Communications, Networking and Mobile Computing ◽

10.1109/wicom.2006.377 ◽

2006 ◽

Author(s):

Feng Hai-Liang ◽

Chen Di ◽

Lin Qing-Jia ◽

Chen Chun-Xiao

Keyword(s):

Neural Network ◽

Network Traffic ◽

Traffic Prediction ◽

Combined Model ◽

Two Stage ◽

Multi Scale ◽

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Small-time Scale Network Traffic Prediction Based on Complex-valued Neural Network

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/224/1/012044 ◽

2017 ◽

Vol 224 ◽

pp. 012044 ◽

Author(s):

Bin Yang

Keyword(s):

Neural Network ◽

Network Traffic ◽

Traffic Prediction ◽

Scale Network ◽

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A Visualization Tool for Exploring Multi-scale Network Traffic Anomalies

Journal of Networks ◽

10.4304/jnw.6.4.577-586 ◽

2011 ◽

Vol 6 (4) ◽

Author(s):

Romain Fontugne ◽

Toshio Hirotsu ◽

Kensuke Fukuda

Keyword(s):

Network Traffic ◽

Visualization Tool ◽

Multi Scale ◽

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Network traffic prediction based on multi-scale wavelet transform and mixed time series model

2014 9th International Conference on Computer Science & Education ◽

10.1109/iccse.2014.6926551 ◽

2014 ◽

Author(s):

Tan Hongjian ◽

Yang Yahui

Keyword(s):

Time Series ◽

Wavelet Transform ◽

Network Traffic ◽

Time Series Model ◽

Traffic Prediction ◽

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Small-time scale network traffic prediction based on a local support vector machine regression model

Chinese Physics B ◽

10.1088/1674-1056/18/6/014 ◽

2009 ◽

Vol 18 (6) ◽

pp. 2194-2199 ◽

Author(s):

Meng Qing-Fang ◽

Chen Yue-Hui ◽

Peng Yu-Hua

Keyword(s):

Support Vector Machine ◽

Regression Model ◽

Network Traffic ◽

Traffic Prediction ◽

Support Vector ◽

Support Vector Machine Regression ◽

Local Support ◽

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Ensemble of Flexible Neural Tree and Ordinary Differential Equations for Small-time Scale Network Traffic Prediction

Journal of Computers ◽

10.4304/jcp.8.12.3039-3046 ◽

2013 ◽

Vol 8 (12) ◽

Author(s):

Bin Yang ◽

Mingyan Jiang ◽

Yuehui Chen ◽

Qingfang Meng ◽

Ajith Abraham

Keyword(s):

Differential Equations ◽

Ordinary Differential Equations ◽

Network Traffic ◽

Traffic Prediction ◽

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Small-time scale network traffic prediction based on flexible neural tree

Applied Soft Computing ◽

10.1016/j.asoc.2011.08.045 ◽

2012 ◽

Vol 12 (1) ◽

pp. 274-279 ◽

Author(s):

Yuehui Chen ◽

Bin Yang ◽

Qingfang Meng

Keyword(s):

Network Traffic ◽

Traffic Prediction ◽

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Multi-scale high-speed network traffic prediction using combination of neural networks

Proceedings of the International Joint Conference on Neural Networks, 2003. ◽

10.1109/ijcnn.2003.1223839 ◽

2004 ◽

Author(s):

A. Khotanzad ◽

N. Sadek

Keyword(s):

Neural Networks ◽

Network Traffic ◽

Traffic Prediction ◽

Multi Scale ◽

High Speed Network

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A comprehensive review on hybrid network traffic prediction model

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i2.pp1450-1459 ◽

2021 ◽

Vol 11 (2) ◽

pp. 1450

Author(s):

Jinmei Shi ◽

Yu Beng Leau ◽

Kun Li ◽

Joe Henry Obit

Keyword(s):

Network Traffic ◽

Prediction Models ◽

Nonlinear Models ◽

Past Research ◽

Fast Response ◽

Hybrid Network ◽

Traffic Prediction ◽

Contributing Factors ◽

Multi Scale ◽

Scale Characteristics

Network traffic is a typical nonlinear time series. As such, traditional linear and nonlinear models are inadequate to describe the multi-scale characteristics of traffic, thus compromising the prediction accuracy. Therefore, the research to date has tended to focus on hybrid models rather than the traditional linear and non-linear ones. Generally, a hybrid model adopts two or more methods as combined modelling to analyze and then predict the network traffic. Against this backdrop, this paper will review past research conducted on hybrid network traffic prediction models. The review concludes with a summary of the strengths and limitations of existing hybrid network prediction models which use optimization and decomposition techniques, respectively. These two techniques have been identified as major contributing factors in constructing a more accurate and fast response hybrid network traffic prediction.

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