The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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A Study on the Short Term Internet Traffic Forecasting Models on Long-Memory and Heteroscedasticity

장기기억 특성과 이분산성을 고려한 인터넷 트래픽 예측을 위한 시계열 모형 연구

  • Sohn, H.G. (Department of Applied Statistics, Chung-Ang University) ;
  • Kim, S. (Department of Applied Statistics, Chung-Ang University)
  • 손흥구 (중앙대학교 응용통계학과) ;
  • 김삼용 (중앙대학교 응용통계학과)
  • Received : 2013.11.26
  • Accepted : 2013.12.24
  • Published : 2013.12.31
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Abstract

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.

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.

Keywords

References

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Cited by

  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