| 粗粒度网络流量的灰色模型预测 |
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| 引用本文： | 孙韩林,金跃辉,崔毅东,程时端.粗粒度网络流量的灰色模型预测[J].北京邮电大学学报,2010,33(1):7-11. |
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| 作者姓名： | 孙韩林 金跃辉 崔毅东 程时端 |
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| 作者单位： | 北京邮电大学,网络与交换技术国家重点实验室,北京,100876;北京邮电大学,网络与交换技术国家重点实验室,北京,100876;北京邮电大学,信息与通信工程学院,北京,100876 |
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| 基金项目： | 国家重点基础研究发展计划项目(2009CB320505,2009CB320504);;国家高技术研究发展计划项目(2006AA01Z235,2007AA01Z206,2009AA01Z210);;高等学校博士学科点专项科研基金项目(200800131019) |
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| 摘 要： | 在实际网络流量上研究了新陈代谢灰色模型(MGM)预测流量. 预测结果表明，灰色模型建模长度远小于流量序列主周期长度时，预测精度较高. 灰色模型预测流量宜采用小量数据建模，此时残差修正对提高预测精度影响很小,预测不需采用残差灰色模型(RGM). 对比了灰色模型与自回归综合滑动平均模型（ARIMA）和Elman神经网络（ENN）模型的预测结果，灰色模型远优于ARIMA，与ENN相当. 灰色模型的优点是能自适应网络流量的变化.
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| 关 键 词： | 网络流量预测 灰色理论 灰色模型 |
| 收稿时间： | 2009-5-18 |
| 修稿时间： | 2009-11-30 |
Large-Time Scale Network Traffic Short-Term Prediction by Grey Model |
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| SUN Han-lin,JIN Yue-hui,CUI Yi-dong,CHENG Shi-duan.Large-Time Scale Network Traffic Short-Term Prediction by Grey Model[J].Journal of Beijing University of Posts and Telecommunications,2010,33(1):7-11. |
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| Authors: | SUN Han-lin JIN Yue-hui CUI Yi-dong CHENG Shi-duan |
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| Affiliation: | (1.State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876， China； 2.School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876， China) |
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| Abstract: | The Metabolic Grey Model (MGM) was investigated for network traffic prediction. The results show when the MGM modeling-length is far shorter than the traffic primary-period length, the accuracy is satisfactory. Small modeling-length MGM is preferred. Residual Grey Model (RGM) is not needed for its contribution to accuracy improvement is limited. The prediction of MGM, ARIMA and Elman Neural Network(ENN) were compared. The MGM accuracy is far better than the that of ARIMA, and equals to that of ENN. MGM is adaptive to traffic changes. |
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| Keywords: | network traffic prediction grey theory grey model |
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