Network Traffic Short-Term Prediction Based on Echo State Network Optimized by Improved Black Hole Algorithm

doi:10.12068/j.issn.1005-3026.2018.03.002

Abstract

Abstract: The network traffic data series has chaos characteristics. After phase space reconstruction， a nonlinear prediction model based on echo state network (ESN) optimized by improved black hole (BH) was used to predict network traffic. The improved BH algorithm is a new mechanism for new-solution generation based on current works， which can increase the algorithms convergence speed and precision. Compared with other optimization algorithms， such as genetic algorithm (GA)， harmony search (HS) algorithm， etc， the proposed improved BH algorithm is not affected by the accuracy of the setting for some parameters of itself. It is used to optimally select four key parameters of the ESN model， which has better prediction stability. Simulation experiments of Mackey-Glass chaos time series and public network traffic data set show that the proposed method has better prediction ability.

Key words: network traffic, chaos time series, echo state network(ESN), black hole algorithm, prediction

CLC Number:

TP393

HAN Ying， JING Yuan-wei， JIN Jian-yu， LI Kun. Network Traffic Short-Term Prediction Based on Echo State Network Optimized by Improved Black Hole Algorithm[J]. Journal of Northeastern University Natural Science, 2018, 39(3): 311-315.

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