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Forecasting internet traffic by using seasonal GARCH models

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1 Author(s)
Sahm Kim ; Department of Applied Statistics, Chung-Ang University, 221 Heukseok-Dong, Dongjak-Gu, Seoul 156-756, Korea

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.

Published in:

Journal of Communications and Networks  (Volume:13 ,  Issue: 6 )