Large-Time Scale Network Traffic Short-Term Prediction by Grey Model

doi:10.13190/jbupt.201001.7.sunhl

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2010, Vol. 33 ›› Issue (1): 7-11.doi: 10.13190/jbupt.201001.7.sunhl

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Large-Time Scale Network Traffic Short-Term Prediction by Grey Model

SUN Han-lin¹；JIN Yue-hui¹；CUI Yi-dong^1,2；CHENG Shi-duan¹

(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)

Received:2009-05-18 Revised:2009-11-30 Online:2010-02-28 Published:2010-02-28
Contact: SUN Han-lin

Abstract

Abstract:

The metabolic grey model (MGM) is investigated for network traffic prediction. It is shown that, when the MGM modeling-length is 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, auto regressive integrated moving average（ARIMA ）and Elman neural network(ENN) are compared. The MGM accuracy is better than the ARIMAs, and equals to the ENNs. Furthermore, MGM is adaptive to traffic changes.

Key words: network traffic prediction, grey theory, grey model

SUN Han-lin¹；JIN Yue-hui¹；CUI Yi-dong^1,2；CHENG Shi-duan¹. Large-Time Scale Network Traffic Short-Term Prediction by Grey Model[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2010, 33(1): 7-11.

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Large-Time Scale Network Traffic Short-Term Prediction by Grey Model

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