Network Traffic Prediction Based on Wavelet Transform and Extreme Learning Machine

HU Jing-wei

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Network Traffic Prediction Based on Wavelet Transform and Extreme Learning Machine

更新时间：2026-09-04

- Network Traffic Prediction Based on Wavelet Transform and Extreme Learning Machine
- Vol. 34, Issue 6, Pages: 132-136(2017)
- 作者机构：
  
  河套学院机构理学系, 内蒙古巴彦淖尔,015000
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2017
- 稿件说明：
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胡竟伟. 基于WA-ELM的网络流量混沌预测模型[J]. 微电子学与计算机, 2017, 34(6): 132-136.

HU Jing-wei. Network Traffic Prediction Based on Wavelet Transform and Extreme Learning Machine[J]. 2017, 34(6): 132-136.
胡竟伟. 基于WA-ELM的网络流量混沌预测模型[J]. 微电子学与计算机, 2017, 34(6): 132-136. DOI：

HU Jing-wei. Network Traffic Prediction Based on Wavelet Transform and Extreme Learning Machine[J]. 2017, 34(6): 132-136. DOI：

摘要

针对当前网络流量预测模型存在的缺陷

结合网络流量的混沌特性

提出了小波变换和极限学习机的网络流量预测模型(WA-ELM).首先采用小波变换对网络流量时间序列进行处理

得到不同频率特征的分量

并对各特征分量进行相空间重构

然后采用极限学习机对网络流量各分量进行建模与预测

并对网络流量分量的预测值进行小波重构

得到原始网络流量的预测值

最后采用具体网络流量预测结果进行了验证

并与其他模型进行了对照测试.结果表明

WA-ELM获得了比其他模型更高的网络流量预测精度

而且网络流量的预测结果更加稳定

为网络流量提供了一种新的建模工具.

Abstract

Aiming at the defects of the current network traffic prediction model

a novel network traffic prediction model based on wavelet transform and extreme learning machine is proposed. At first

wavelet transform is used to process time series of network traffic to get different frequency components

and each feature is used to be reconstructed by phase space

and secondly extreme learning machine is used to model and predict network traffic of each component

and predicted value of each component are reconstructed by traffic by wavelet transform to get network traffic prediction results

at last

the performance is tested by network traffic data and compared with other models The results show that the proposed model can get higher accurate prediction results than other models

and the prediction results of network traffic are more stable

it provides modeling tool for network traffic.

关键词

Keywords

references

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