中国物理B ›› 2009, Vol. 18 ›› Issue (6): 2194-2199.doi: 10.1088/1674-1056/18/6/014

• • 上一篇    下一篇

Small-time scale network traffic prediction based on a local support vector machine regression model

彭玉华1, 陈月辉2, 孟庆芳3   

  1. (1)School of Information Science and Engineering, Shandong University, Jinan 250100, China; (2)School of Information Science and Engineering, University of Jinan, Jinan 250022, China; (3)School of Information Science and Engineering, University of Jinan, Jinan 250022, China;School of Information Science and Engineering, Shandong University, Jinan 250100, China
  • 收稿日期:2008-10-30 修回日期:2008-11-30 出版日期:2009-06-20 发布日期:2009-06-20
  • 基金资助:
    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).

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)   

  1. a School of Information Science and Engineering, University of Jinan, Jinan 250022, China; b School of Information Science and Engineering, Shandong University, Jinan 250100, China
  • Received:2008-10-30 Revised:2008-11-30 Online:2009-06-20 Published:2009-06-20
  • Supported by:
    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).

PDF (PC)

979

赞

摘要： 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.

关键词: network traffic, small-time scale, nonlinear time series analysis, support vector machine regression model

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.

Key words: network traffic, small-time scale, nonlinear time series analysis, support vector machine regression model

中图分类号:  (Granular models of complex systems; traffic flow)

  • 45.70.Vn
02.70.Rr (General statistical methods) 05.45.Tp (Time series analysis) 89.75.Hc (Networks and genealogical trees)