Journal of Communications and Networks

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Forecasting Internet Traffic by Using Seasonal GARCH Models

  • Kim, Sahm (Department of Applied Statistics, Chung-Ang University)
  • Received : 2011.05.09
  • Published : 2011.12.31
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Abstract

With the rapid growth of internet traffic, accurate and reliable prediction of internet traffic has been a key issue in network management and planning. This paper proposes an autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) error model for forecasting internet traffic and evaluates its performance by comparing it with seasonal autoregressive integrated moving average (ARIMA) models in terms of root mean square error (RMSE) criterion. The results indicated that the seasonal AR-GARCH models outperformed the seasonal ARIMA models in terms of forecasting accuracy with respect to the RMSE criterion.

Keywords

References

  1. G. E. P. Box and G. Jenkins, Time Series Analysis: Forecasting and Control. Prentice Hall, 1994.
  2. L. Jiakun, S. Yantai, Z. Lianfang, and X. Fei, "Traffic modeling based on FARIMA models," in Proc. CCECE, vol. 1, 1999, pp. 162-167.
  3. Y. Shu, M. Yu, O. Yang, J. Liu, and H. Feng, "Wireless traffic modeling and prediction using seasonal ARIMA models," IEICE Trans. Commun., pp.1675-1679, 2005.
  4. B. Krithikaivasan, Y. Zeng, K. Deka, and D. Medhi, "ARCH-based traffic forecasting and dynamic bandwidth provisioning for periodically measured nonstationary traffic," IEEE/ACM Trans. Netw., vol. 15, pp. 683- 696, June 2007. https://doi.org/10.1109/TNET.2007.893217
  5. R. F. Engle, "Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation," Econometrica, vol. 50, pp. 987-1007, 1982. https://doi.org/10.2307/1912773
  6. B. Zhou, D. He, Z. Sun, and W. H. Ng, "Network traffic modeling and prediction with ARIMNGARCH," in Proc. HET-NETS, 2005, p. 8.
  7. Y. Shu, Z. Jin, L. Zhang, and L. Wang, "Traffic prediction using FARIMA models," in Proc. IEEE ICC, 1999, pp. 891-895.
  8. H. Akaike, "A new look at the statistical model identification," IEEE Trans. Autom. Control, vol. 19, pp. 716-723,1974. https://doi.org/10.1109/TAC.1974.1100705
  9. T. Bollerlev, "Generalized autoregressive conditional heteroskedasticity," J. Econometrics, vol. 31, pp. 307-327,1986. https://doi.org/10.1016/0304-4076(86)90063-1
  10. T. S. Breusch and A. R. Pagan, "The Lagrange multiplier test and its applications to model specification in econometrics," Review of Economic Studies, vol. 47, pp. 239-254, 1980. https://doi.org/10.2307/2297111
  11. B. Krithikaivasan, Y. Zeng, and D. Medhi, "Generalized cost function based forecasting for periodically measured nonstationary traffic," IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 38, pp. 1105-1117, Sept. 2008. https://doi.org/10.1109/TSMCA.2008.2001077
  12. D. Tikunov, Software, Telecommunications, and Computer Networks. SoftCOM, pp. 1-5, 2007.
  13. J. Caiado, "Performance of combined double seasonal univariate time series models for forecasting water demand," J. Hydrologic Eng., vol. 15, pp. 215-222, 2010. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000182
  14. K. Papagiannaki, N. Taft, Z. Zhang, and C. Diot, "Long-term forecasting of Internet backbone traffic: Observations and initial models," in Proc. IEEE INFOCOM, 1999, pp. 1178-1188.