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