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基于ARIMA补偿ELM的网络流量预测方法
  • ISSN号：1002-0411
  • 期刊名称：《信息与控制》
  • 时间：0
  • 分类：TP393[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[1]沈阳工业大学信息科学与工程学院,辽宁沈阳110870
  • 相关基金：国家自然科学基金重点基金资助项目（61034005）
作者： 田中大[1], 李树江[1], 王艳红[1], 王向东[1]
关键词： 网络流量, 预测, 极限学习机, 自回归积分滑动平均模型, (ARIMA), 自相似性, network traffic , prediction , extreme learning machineautoregressive integratedmoving average model（ARIMA） , self-similarity
中文摘要：

针对网络流量的预测问题，结合网络流量序列的自相似性分析而提出一种基于差分自回归滑动平均模型（ARIMA）补偿极限学习机（ELM）的网络流量预测方法．首先利用ELM模型对网络流量序列进行一步预测，然后对网络流量预测的误差序列通过ARIMA模型进行修正，最后将ELM模型预测值与ARIMA模型修正值进行叠加得到最终的预测值。与单独的ARIMA模型、最小二乘支持向量机（LS—SVM）预测模型以及Elman神经网络预测模型进行了对比，仿真结果表明本文的方法具有更高的预测精度。

英文摘要：

For the problem of network traffic prediction, we propose a network traffic prediction method based on an autoregressive integrated moving average model （ARIMA） compensation extreme learning machine （ELM）, combining the network traffic sequence self-similar analysis. Firstly, one-step network traffic is precJictecl by the extreme learning machine, and then the sequence of network traffic forecast error is corrected by the ARI- MA model. Finally, we obtain the network traffic by adding the predictive values of ELM and ARIMA. We compare the proposed ARIMA method with least squares support vector machine and Elman neural network pre- diction model. Simulation results show that the proposed method has higher prediction accuracy than the either.

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期刊信息
  • 《信息与控制》
  • 北大核心期刊（2011版）
  • 主管单位:中国科学院
  • 主办单位:中国自动化学会 中国科学院沈阳自动化研究所
  • 主编：王天然
  • 地址：沈阳市南塔街114号
  • 邮编：110016
  • 邮箱：xk@sia.cn
  • 电话：024-23970049
  • 国际标准刊号：ISSN：1002-0411
  • 国内统一刊号：ISSN：21-1138/TP
  • 邮发代号:
  • 获奖情况:
  • 全国优秀期刊三等奖,中科院优秀期刊三等奖,辽宁省优秀期刊一等奖
  • 国内外数据库收录:
  • 美国数学评论（网络版）,荷兰文摘与引文数据库,英国科学文摘数据库,日本日本科学技术振兴机构数据库,中国中国科技核心期刊,中国北大核心期刊（2004版）,中国北大核心期刊（2008版）,中国北大核心期刊（2011版）,中国北大核心期刊（2014版）,中国北大核心期刊（2000版）
  • 被引量:12960