응용통계연구 (The Korean Journal of Applied Statistics)

  • 제26권6호
  • /
  • Pages.1053-1061
  • /
  • 2013
  • /
  • 1225-066X(pISSN)
  • /
  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
권호 표지
DOI QR코드

DOI QR Code

장기기억 특성과 이분산성을 고려한 인터넷 트래픽 예측을 위한 시계열 모형 연구

A Study on the Short Term Internet Traffic Forecasting Models on Long-Memory and Heteroscedasticity

  • 손흥구 (중앙대학교 응용통계학과) ;
  • 김삼용 (중앙대학교 응용통계학과)
  • Sohn, H.G. (Department of Applied Statistics, Chung-Ang University) ;
  • Kim, S. (Department of Applied Statistics, Chung-Ang University)
  • 투고 : 2013.11.26
  • 심사 : 2013.12.24
  • 발행 : 2013.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문은, 장기기억 특성과 이분산성을 고려한 인터넷 트래픽 예측 모형을 제안하고자 한다. 트래픽 과부하를 대비하기 위해서, 트래픽 용량은 트래픽의 예측치와 트래픽의 변동 크기에 따라 트래픽의 최대용량을 설정하여야 한다. 이를 위하여 교내 트래픽 자료 중 교내로 들어오는 트래픽과 교외로 나가는 트래픽에 이분산성과 장기기억 모형의 유용성을 확인하였다. 이에 대하여 AR-GARCH 모형, ARMA-GARCH 모형과 장기기억모형인 Fractional ARIMA와 장기기억과 이분산성을 고려한 Fractional ARMA-GARCH 모형을 적용하여 모형의 예측성능을 비교하였다.

In this paper, we propose the time series forecasting models for internet traffic with long memory and heteroscedasticity. To control and forecast traffic volume, we first introduce the traffic forecasting models which are determined by the volatility and heteroscedasticity of the traffic. We then analyze and predict the heteroscedasticity and the long memory properties for forecasting traffic volume. Depending on the characteristics of the traffic, Fractional ARIMA model, Fractional ARIMA-GARCH model are applied and compared with the MAPE(Mean Absolute Percentage Error) Criterion.

키워드

참고문헌

  1. Basu, A., Mukherjee, A. and Klivansky, S. (1996). Time series models for internet traffic, In Proceedings IEEE INFOCOM 96, Fifteenth Annual Conference of the IEEE Computer Societies, 4, 24-28.
  2. Baillie, Richard T. Chung, Ching-Fan.Tieslau, Margie A.(1996). Analysing inflation by the fractionally integrated FARIMA-GARCH model, Journal of applied econometrics, 11, 23-40. https://doi.org/10.1002/(SICI)1099-1255(199601)11:1<23::AID-JAE374>3.0.CO;2-M
  3. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of econometrics, 31, 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
  4. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of U. K. Inflation, Econometrica, 50, 987-1008. https://doi.org/10.2307/1912773
  5. Jiang, J. and Papavassiliou, S. (2006). Enhancing network traffic prediction and anomaly detection via statistical network traffic separation and combination strategies, Computer communications, 29(10), 1627-1638. https://doi.org/10.1016/j.comcom.2005.07.030
  6. Kim, S. (2007). Time series models for performance evaluation of network traffic forecasting, The Korea Journal of Applied Statistics, 20, 219-227. https://doi.org/10.5351/KJAS.2007.20.2.219
  7. Kim, S. (2011). Forecasting internet traffic by using seasonal GARCH models, Journal of Communications and Network, 13, 621-624. https://doi.org/10.1109/JCN.2011.6157478
  8. Liu, J., Shu, Y., Zhang, L. and Xue, F. (1999). Traffic modeling based on FARIMA models, IEEE Can. Conf. on Electrical and Computer Engineering, 162-167.
  9. Shu, Y., Yu, M., Yang, O., Liu, J. and Feng, H. (2005). Wireless traffic modeling and prediction using seasonal ARIMA models, IEICE-transactions on Communications, 10, 3992-3999.
  10. Tikunov, D. and Nishimura, T. (2007). Traffic Prediction for Mobile Network using Holt-Winter's Exponential Smoothing, Telecommunications and Computer Networks, 15th International Conference, 1-5.

피인용 문헌

  1. A Study on Internet Traffic Forecasting by Combined Forecasts vol.28, pp.6, 2015, https://doi.org/10.5351/KJAS.2015.28.6.1235