Browse > Article

Forecasting Internet Traffic by Using Seasonal GARCH Models  

Kim, Sahm (Department of Applied Statistics, Chung-Ang University)
Publication Information
Journal of Communications and Networks / v.13, no.6, 2011 , pp. 621-624 More about this Journal
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
Akaike information criterion (AIC); Internet traffic; root mean square error (RMSE); seasonal autoregressive-generalized autoregressive conditional heteroscedasticity (ARGARCH); seasonal autoregressive integrated moving average (ARIMA);
Citations & Related Records

Times Cited By Web Of Science : 0  (Related Records In Web of Science)
연도 인용수 순위
  • Reference
1 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.   DOI
2 R. F. Engle, "Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation," Econometrica, vol. 50, pp. 987-1007, 1982.   DOI   ScienceOn
3 B. Zhou, D. He, Z. Sun, and W. H. Ng, "Network traffic modeling and prediction with ARIMNGARCH," in Proc. HET-NETS, 2005, p. 8.
4 Y. Shu, Z. Jin, L. Zhang, and L. Wang, "Traffic prediction using FARIMA models," in Proc. IEEE ICC, 1999, pp. 891-895.
5 H. Akaike, "A new look at the statistical model identification," IEEE Trans. Autom. Control, vol. 19, pp. 716-723,1974.   DOI
6 T. Bollerlev, "Generalized autoregressive conditional heteroskedasticity," J. Econometrics, vol. 31, pp. 307-327,1986.   DOI   ScienceOn
7 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.   DOI   ScienceOn
8 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.   DOI
9 D. Tikunov, Software, Telecommunications, and Computer Networks. SoftCOM, pp. 1-5, 2007.
10 J. Caiado, "Performance of combined double seasonal univariate time series models for forecasting water demand," J. Hydrologic Eng., vol. 15, pp. 215-222, 2010.   DOI   ScienceOn
11 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.
12 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.
13 G. E. P. Box and G. Jenkins, Time Series Analysis: Forecasting and Control. Prentice Hall, 1994.
14 L. Jiakun, S. Yantai, Z. Lianfang, and X. Fei, "Traffic modeling based on FARIMA models," in Proc. CCECE, vol. 1, 1999, pp. 162-167.