CUI Yidong, SUN Hanlin. Periodicity Impacts on the Accuracy in GreyModel Based Internet Tra±c Prediction[J]. Chinese Journal of Electronics, 2010, 19(1): 170-174.
Citation: CUI Yidong, SUN Hanlin. Periodicity Impacts on the Accuracy in GreyModel Based Internet Tra±c Prediction[J]. Chinese Journal of Electronics, 2010, 19(1): 170-174.

Periodicity Impacts on the Accuracy in GreyModel Based Internet Tra±c Prediction

  • Received Date: 1900-01-01
  • Rev Recd Date: 1900-01-01
  • Publish Date: 2010-01-05
  • 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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