吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (4): 1254-1260.doi: 10.13229/j.cnki.jdxbgxb201604035

• Orginal Article • Previous Articles     Next Articles

LAN traffic flow prediction using improved small data method

WANG Shi1, 2, 3, SUI Yong-xin1, DONG Yan3, YANG Huai-jiang1   

  1. 1.Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences,Changchun 130033,China;
    2.University of Chinese Academy of Sciences,Beijing 100039,China;
    3. Office of Information Management and Planning,Northeast Normal University,Changchun 130024,China
  • Received:2014-11-02 Online:2016-07-20 Published:2016-07-20

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Abstract: First, the LAN traffic flow time series are reconstructed in the phase space using Takens theory. Then the embedding dimension and delay time are calculated via the C-C algorithm. Third, the average period is calculated via the frequency weighting derived from the power and averaging method. With the above steps the improved small data method becomes more complete. The improved small data method is applied to calculate the largest Lyapunov exponent of the chaos time series of the Lorenz system and the prediction data of the real measured LAN traffic flow time series. Results show that the LAN peak traffic flow is chaotic and the prediction based on the improved small data method is more accurate, faster, and more points can be predicted.

Key words: computer application, chaos time series, small data method, LAN traffic flow prediction

CLC Number: 

  • TP393
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