Title of article :
Time-series forecasting using a system of ordinary differential equations
Author/Authors :
Yuehui Chen، نويسنده , , Bin Yang، نويسنده , , Qingfang Meng، نويسنده , , Yaou Zhao، نويسنده , , Ajith Abraham، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2011
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 optimized using PSO algorithm. Experimental results reveal that the proposed method is feasible and efficient for forecasting the small-scale traffic measurements data.
Keywords :
Hybrid evolutionary method , network traffic , Small-time scale , The additive tree models , ordinary differential equations , particle swarm optimization
Journal title :
Information Sciences
Journal title :
Information Sciences