Paper Title:
Joint Network Traffic Forecast with ARIMA Models and Chaotic Models Based on Wavelet Analysis
  Abstract

In the recent study of network traffic, it is shown that the traffic flow presents both periodic and self-similar characteristics. Due to these two features, the short-term forecast of network traffic cannot be accurately fit in either autoregressive integrated moving average (ARIMA) models which is suitable for linear behavior, or chaotic models which is corresponding to self-similarity characteristic. In this paper, our methodology suggests that by using wavelet multiresolution analysis, we can obtain a joint short-term network traffic prediction method and get a more precise forecast result as compared to using either ARIMA models or chaotic models. We also run simulations to show the improvement of prediction accuracy of our proposed approach.

  Info
Periodical
Edited by
Qi Luo
Pages
743-746
DOI
10.4028/www.scientific.net/AMM.55-57.743
Citation
M. K. Dong, C. Chen, M. H. Huang, Y. Jin, "Joint Network Traffic Forecast with ARIMA Models and Chaotic Models Based on Wavelet Analysis", Applied Mechanics and Materials, Vols. 55-57, pp. 743-746, 2011
Online since
May 2011
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