A Novel Procedure to Model and Forecast Mobile Communication Traffic by ARIMA/GARCH Combination Models

Mobile traffic modeling and forecasting are the key techniques in terms of network optimization and management because better network management can be achieved through improving the forecasting accuracy. While mobile traffic has been studied extensively and proved to be effectively modeled with ARIMA models, the volatility effect in mobile traffic series that results in forecasting errors was seldom mentioned. In this study, a multiplicative seasonal ARIMA/GARCH building procedure is proposed to show that volatility effect appearing in mobile traffic series can be processed by GARCH models. Our proposed procedure combines several evaluating parameters such as Akaike Information Criterion (AIC), Schwarz Criterion (SIC), forecast performance evaluation information and residual correlogram to find out the most suitable model, based on which descriptive statistics are used to get the final choice. This work indicates that the mobile traffic series can be better modeled and forecasted by applying GARCH models based on a multiplicative seasonal ARIMA. Keywords-ARIMA; GARCH; GARCH-M; traffic forecasting, traffic modeling

[1]  Qiang Meng,et al.  Short-time traffic flow prediction with ARIMA-GARCH model , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[2]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[3]  Russell P. Robins,et al.  Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model , 1987 .

[4]  L. Glosten,et al.  On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks , 1993 .

[5]  Yu Peng,et al.  Traffic forecasting for mobile networks with multiplicative seasonal ARIMA models , 2009, 2009 9th International Conference on Electronic Measurement & Instruments.

[6]  Daniel B. Nelson.  CONDITIONAL HETEROSKEDASTICITY IN ASSET RETURNS: A NEW APPROACH , 1991 .

[7]  Hengchao Li,et al.  A Multiplicative Seasonal ARIMA/GARCH Model in EVN Traffic Prediction , 2015 .

[8]  R. Chou,et al.  ARCH modeling in finance: A review of the theory and empirical evidence , 1992 .

[9]  Qingqi Pei,et al.  Forecasting 802.11 Traffic Using Seasonal ARIMA Model , 2009, 2009 International Forum on Computer Science-Technology and Applications.

[10]  Xiaowei Qin,et al.  The periodic data traffic modeling based on multiplicative seasonal ARIMA model , 2014, 2014 Sixth International Conference on Wireless Communications and Signal Processing (WCSP).

[11]  Lei Shen,et al.  Prediction of Network Flow Based on Wavelet Analysis and ARIMA Model , 2009, 2009 International Conference on Wireless Networks and Information Systems.

[12]  Sahm Kim.  Forecasting internet traffic by using seasonal GARCH models , 2011, Journal of Communications and Networks.

[13]  Marco van Akkeren,et al.  A GARCH forecasting model to predict day-ahead electricity prices , 2005, IEEE Transactions on Power Systems.

[14]  C. Granger,et al.  A long memory property of stock market returns and a new model , 1993 .