东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2011, Vol. 32 ›› Issue (10): 1382-1385+1389.DOI: -

• 论著 • 上一篇    下一篇

基于小波变换与组合模型的网络流量预测算法

魏永涛;汪晋宽;王翠荣;张琨;   

  1. 东北大学信息科学与工程学院;
  • 收稿日期:2013-06-19 修回日期:2013-06-19 发布日期:2013-04-04
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(60874108,60904035)

Network traffic prediction algorithm based on wavelet transform and combinational models

Wei, Yong-Tao (1); Wang, Jin-Kuan (1); Wang, Cui-Rong (1); Zhang, Kun (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
  • Received:2013-06-19 Revised:2013-06-19 Published:2013-04-04
  • Contact: Wei, Y.-T.
  • About author:-
  • Supported by:
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摘要： 针对网络流量在以不同时间尺度分析时呈现不同特性给流量精确预测带来的困难,提出一种基于快速小波变换和季节差分自回归滑动平均组合模型的多分辨分析预测算法.采用小波方法对网络历史流量进行分解以分析不同时间尺度下的流量相关结构,根据不同时间尺度下的流量时间序列的周期性和自相似性,分别选择合适的模型建模用于预测.使用小波方法对各序列的预测值进行重构,得到原始流量的预测结果.仿真结果表明,所提预测方法比同类预测方法具有更高的精度.

关键词: 网络流量, 预测, 小波变换, 组合模型

Abstract: For the multi-scale characteristic of network traffic, a prediction algorithm based on wavelet transform and combinational SARIMA model was introduced. The network history traffic was decomposed with wavelet method to analyze complex correlation structure of the network traffic. According to the periodicity and self-similarity of the traffic series under different time scales, different prediction models were selected for prediction. The resulted series was reconstructed with wavelet method to get the results of original traffic. Simulation results showed that the proposed method can achieve higher prediction accuracy than that of the similar prediction methods.

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