Computer Science ›› 2015, Vol. 42 ›› Issue (4): 68-71.doi: 10.11896/j.issn.1002-137X.2015.04.012

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Traffic Prediction Algorithm in Buffer Based on Recurrence Quantification Union Entropy Feature Reconstruction

LU Xing-hua and CHEN Ping-hua   

  • Online:2018-11-14 Published:2018-11-14

Abstract: The accurate prediction of short-time traffic network in base station buffer is the key to alleviate and control congestion.The short-time traffic network traffic has nonlinear chaotic characteristics,and the self correlation is weak.The traditional method uses linear time series method,and the nonlinear feature information is not used,so the prediction performance is not good.An improved traffic prediction algorithm based on nonlinear time series analysis and recurrence quantification union entropy feature reconstruction was proposed.The union entropy feature is extracted.The phase space reconstruction of characteristic sequences is obtained.The signal is mapped in the high dimensional phase space,and the recurrence quantitative analysis is taken for the traffic series.The autocorrelation characteristic singular decomposition is used for linear superposition after polymerization on the runoff series.The average mutual information method and false nearest neighbor algorithm are used for parameters optimization.Interpolation is taken for time frequency analysis and traffic flow control.The prediction of network traffic is completed.Simulation results show that this algorithm has high prediction accuracy and good stability,and prediction error is lower than traditional method,which shows good application value.

Key words: Base station buffers,Network traffic,Prediction,Nonlinear feature

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