Network traffic nonlinear prediction combined with mutifractal

WANG Sheng-hui; QIU Zheng-ding

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Network traffic nonlinear prediction combined with mutifractal

更新时间：2024-10-14

- Network traffic nonlinear prediction combined with mutifractal
- Issue 2, Pages: 45-50(2007)
- 作者机构：
  
  1. 北京交通大学信息所
  2. 北京交通大学信息所,北京,100044
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN915.06
- Published：2007
- 稿件说明：
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WANG Sheng-hui, QIU Zheng-ding. Network traffic nonlinear prediction combined with mutifractal[J]. 2007, (2): 45-50.
DOI：

WANG Sheng-hui, QIU Zheng-ding. Network traffic nonlinear prediction combined with mutifractal[J]. 2007, (2): 45-50. DOI：

摘要

通过分析树型多重分形结构的相关性发现

多重分形可以把非平稳且具有长相关(LRD)和分形特性的网络流量序列转化为可用短相关(SRD)模型表示的序列组。利用多重分形这种将时间序列分解为多层的能力

提出了一种结合多重分形的FIR神经网络流量预测模型(MF-FIR

multifractal FIR network)。MF-FIR合理地利用了流量序列的LRD信息

具有很好的多步预测性能

可以满足通信系统在线预测的要求。

Abstract

By analyzing the correlation structure of multifractal tree

it was found that multifractal has the ability to con-vert the non-stationary long-range dependence（LRD） trace to a series of short-range dependence（SRD） sequence.Based on this property

a FIR neural network traffic predictor combined with multifractal was proposed.Because the LRD fea-ture of trace is used

the multi-step performance of proposed method is much better than traditional methods.

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Related Institution

School of Statistics and Data Science, Zhejiang Gongshang University

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School of Statistics and Data Science, Zhejiang Gongshang University, Hangzhou 310018, China

School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou 310018, China

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