¹School of Information Science and Engineering, Shandong University, Jinan, 250100, P.R. China
²School of Information Science and Engineering, University of Jinan, 106 Jiwei Road, Jinan, 250022, P.R. China
³Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence P.O. Box 2259 Auburn, Washington, USA

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Abstract—Accurate models play important roles in capturing the salient characteristics of the network traffic, analyzing and simulating for the network dynamic, and improving the predictive ability for system dynamics. In this study, the ensemble of the flexible neural tree (FNT) and system models expressed by the ordinary differential equations (ODEs) is proposed to further improve the accuracy of time series forecasting. Firstly, the additive tree model is introduced to represent more precisely ODEs for the network dynamics. Secondly, the structures and parameters of FNT and the additive tree model are optimized based on the Genetic Programming (GP) and the Particle Swarm Optimization algorithm (PSO). Finally, the expected level of performance is verified by using the proposed method, which provides a reliable forecast model for small-time scale network traffic. Experimental results reveal that the proposed method is able to estimate the small-time scale network traffic measurement data with decent accuracy.

Index Terms—hybrid evolutionary method, small-time scale network traffic, the additive tree models, ordinary differential equations, ensemble learning

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Cite: Bin Yang, Mingyan Jiang, Yuehui Chen, Qingfang Meng, Ajith Abraham, "Ensemble of Flexible Neural Tree and Ordinary Differential Equations for Small-time Scale Network Traffic Prediction," Journal of Computers vol. 8, no. 11, pp. 3039-3046, 2013.