Paper Info
Title
Ensemble Of Flexible Neural Tree And Ordinary Differential Equations For Small-Time Scale Network Traffic Prediction
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.
Year
DOI
Venue
2013
10.4304/jcp.8.12.3039-3046
JOURNAL OF COMPUTERS
Keywords
Field
DocType
hybrid evolutionary method, small-time scale network traffic, the additive tree models, ordinary differential equations, ensemble learning
Particle swarm optimization,Mathematical optimization,Network dynamics,Ordinary differential equation,Computer science,Decision tree model,Genetic programming,Artificial intelligence,System dynamics,Network traffic measurement,Ensemble learning,Machine learning
Journal
Volume
Issue
ISSN
8
12
1796-203X
Citations 
PageRank 
References 
1
0.34
14
Authors
5
Name
Order
Citations
PageRank
Bin Yang1695.89
Mingyan Jiang26711.96
Yuehui Chen31167106.13
Qingfang Meng49118.63
Ajith Abraham58954729.23