CUI Yidong and SUN Hanlin, “Periodicity Impacts on the Accuracy in GreyModel Based Internet Tra±c Prediction,” Chinese Journal of Electronics, vol. 19, no. 1, pp. 170-174, 2010,
Citation:
CUI Yidong and SUN Hanlin, “Periodicity Impacts on the Accuracy in GreyModel Based Internet Tra±c Prediction,” Chinese Journal of Electronics, vol. 19, no. 1, pp. 170-174, 2010,
CUI Yidong and SUN Hanlin, “Periodicity Impacts on the Accuracy in GreyModel Based Internet Tra±c Prediction,” Chinese Journal of Electronics, vol. 19, no. 1, pp. 170-174, 2010,
Citation:
CUI Yidong and SUN Hanlin, “Periodicity Impacts on the Accuracy in GreyModel Based Internet Tra±c Prediction,” Chinese Journal of Electronics, vol. 19, no. 1, pp. 170-174, 2010,
The Grey model (GM) is used to predict Internet tra±c. The Mean relative error (MRE) of pre- diction varies regularly when one of the parameters for GM(1,1), the Modeling length, increases. Moreover, the prediction error becomes unacceptable in some scenarios. The reason lies in such facts: (1) The Internet backbone tra±c exhibits multi-scale properties in temporal domain, which results in periodical variation of the tra±c sequence; (2) GM (1, 1) requires that the accumulated generating sequence of the data should be the form of exponential function. However, the periodicity of the tra±c sequence violates the condition. In order to keep MRE acceptable, the Modeling length should be far shorter than the Pe- riod length. What's more, the accuracy of four models, ARIMA, ENN, GM (1, 1) and Residual GM (1, 1), was compared and we found that the Residual GM (1, 1) con- tributes little to the prediction accuracy while it doubles the computational complexity.
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