Small-Time Scale Network Traffic Prediction Using Complex Network Models

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Peng Wu and Yuehui Chen and Qingfang Meng and 
                 Zhen Liu",
  title =        "Small-Time Scale Network Traffic Prediction Using
                 Complex Network Models",
  booktitle =    "Fifth International Conference on Natural Computation,
                 ICNC '09",
  year =         "2009",
  month =        aug,
  volume =       "3",
  pages =        "303--307",
  keywords =     "genetic algorithms, genetic programming,
                 autoregressive integrated moving average, complex
                 network models, local approximation, neural network,
                 particle swarm optimization, small time scale network
                 traffic prediction, autoregressive moving average
                 processes, complex networks, neural nets, particle
                 swarm optimisation, telecommunication traffic",
  DOI =          "doi:10.1109/ICNC.2009.122",
  abstract =     "The self-similar and nonlinear nature of network
                 traffic makes high accurate prediction difficult.
                 Various technology, including Autoregressive Integrated
                 Moving Average (ARIMA), Local Approximation (LA),
                 Neural Network (NN) etc., have been applied to Internet
                 traffic prediction. In this paper, Complex Network
                 based on genetic programming and particle swarm
                 optimization is proposed to predict the time series of
                 Internet traffic.We propose an automatic method for
                 constructing and evolving our complex network model.
                 The structure of complex network is evolved using
                 genetic programming, and the fine tuning of the
                 parameters encoded in the structure is accomplished
                 using particle swarm optimization algorithm. The
                 relative performances of our model are reported. The
                 results show that our model has high prediction
                 accuracy and can characterize real network traffic
  notes =        "Also known as \cite{5364488}",

Genetic Programming entries for Peng Wu Yuehui Chen Qingfang Meng Zhen Liu