Small-time scale network traffic prediction based on a local support vector machine regression model
Meng Qing-Fang(孟庆芳)a)b), Chen Yue-Hui(陈月辉)a), and Peng Yu-Hua(彭玉华)b)
aSchool of Information Science and Engineering, University of Jinan, Jinan 250022, China; bSchool of Information Science and Engineering, Shandong University, Jinan 250100, China
Abstract In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.
Received: 30 October 2008
Revised: 30 November 2008
Accepted manuscript online:
PACS:
45.70.Vn
(Granular models of complex systems; traffic flow)
Fund: Project supported by the National
Natural Science Foundation of China (Grant No 60573065), the Natural
Science Foundation of Shandong Province, China (Grant No Y2007G33),
and the Key Subject
Research Foundation of Shandong Province, China (Grant No XTD0708).
Cite this article:
Meng Qing-Fang(孟庆芳), Chen Yue-Hui(陈月辉), and Peng Yu-Hua(彭玉华) Small-time scale network traffic prediction based on a local support vector machine regression model 2009 Chin. Phys. B 18 2194
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