Time-series forecasting using a system of ordinary differential equations

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@Article{Chen2011106,
  author =       "Yuehui Chen and Bin Yang and Qingfang Meng and 
                 Yaou Zhao and Ajith Abraham",
  title =        "Time-series forecasting using a system of ordinary
                 differential equations",
  journal =      "Information Sciences",
  volume =       "181",
  number =       "1",
  pages =        "106--114",
  year =         "2011",
  ISSN =         "0020-0255",
  DOI =          "doi:10.1016/j.ins.2010.09.006",
  URL =          "http://www.sciencedirect.com/science/article/B6V0C-5100HS4-3/2/c9722759c9e35e7dba49e35c559ae617",
  keywords =     "genetic algorithms, genetic programming, PSO, Hybrid
                 evolutionary method, Network traffic, Small-time scale,
                 The additive tree models, Ordinary differential
                 equations, Particle swarm optimisation",
  abstract =     "This paper presents a hybrid evolutionary method for
                 identifying a system of ordinary differential equations
                 (ODEs) to predict the small-time scale traffic
                 measurements data. We used the tree-structure based
                 evolutionary algorithm to evolve the architecture and a
                 particle swarm optimization (PSO) algorithm to fine
                 tune the parameters of the additive tree models for the
                 system of ordinary differential equations. We also
                 illustrate some experimental comparisons with genetic
                 programming, gene expression programming and a
                 feedforward neural network optimised using PSO
                 algorithm. Experimental results reveal that the
                 proposed method is feasible and efficient for
                 forecasting the small-scale traffic measurements
                 data.",
}

Genetic Programming entries for Yuehui Chen Bin Yang Qingfang Meng Yaou Zhao Ajith Abraham

Citations