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

DOI

10.2991/msota-16.2016.8How to use a DOI?

Keywords

ARIMA; GARCH; GARCH-M; traffic forecasting, traffic modeling

Abstract

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Quang Thanh Tran
AU  - Li Hao
AU  - Quang Khai Trinh
PY  - 2016/12
DA  - 2016/12
TI  - A Novel Procedure to Model and Forecast Mobile Communication Traffic by ARIMA/GARCH Combination Models
BT  - Proceedings of 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016)
PB  - Atlantis Press
SP  - 29
EP  - 34
SN  - 2352-538X
UR  - https://doi.org/10.2991/msota-16.2016.8
DO  - 10.2991/msota-16.2016.8
ID  - Tran2016/12
ER  -