Forecasting internet traffic by using seasonal GARCH models PROJECT TITLE :Forecasting internet traffic by using seasonal GARCH modelsABSTRACT: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. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Edge router selection and traffic engineering in LISP-capable networks Recent advances in filter topologies and realizations for satellite communications