Bastos, João A. (2019): Forecasting the capacity of mobile networks. Forthcoming in: Telecommunication Systems
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Abstract
The optimization of mobile network capacity usage is an essential operation to promote positive returns on network investments, prevent capacity bottlenecks, and deliver good end user experience. This study examines the performance of several statistical models to predict voice and data traffic in a mobile network. While no method dominates the others across all time series and prediction horizons, exponential smoothing and ARIMA models are good alternatives to forecast both voice and data traffic. This analysis shows that network managers have at their disposal a set of statistical tools to plan future capacity upgrades with the most effective solution, while optimizing their investment and maintaining good network quality.
Item Type: | MPRA Paper |
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Original Title: | Forecasting the capacity of mobile networks |
Language: | English |
Keywords: | Mobile networks, Forecasting, ARIMA models, Exponential smoothing, Time series |
Subjects: | C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O32 - Management of Technological Innovation and R&D |
Item ID: | 92727 |
Depositing User: | João A. Bastos |
Date Deposited: | 18 Mar 2019 12:54 |
Last Modified: | 29 Sep 2019 14:26 |
References: | Cisco (2017). Cisco Visual Networking Index: Forecast and Methodology, 2016--2021. Makridakis, S., and Hibon, M. (2000). The M-3 Competition: results, conclusions, and implications. International Journal of Forecasting, 16, 451-476. Meade, N., and Islam, T. (2015). Forecasting in telecommunications and ICT - A review. International Journal of Forecasting, 31, 1105-1126. Grambsch, P., and Stahel, W.A. (1990). Forecasting demand for special telephone services - a case study. International Journal of Forecasting}, 6, 53--64. Fildes, R. (1992). The evaluation of extrapolative forecasting methods. International Journal of Forecasting, 8, 81-98. Madden, G., Savage, S.J., and Coble-Neal, G. (2002). Forecasting United States-Asia international message telephone service. International Journal of Forecasting, 18, 523--543. Madden, G., and Coble-Neal, G. (2005). Forecasting international bandwidth capability. Journal of Forecasting, 24, 299-309. Madden, G., and Tan, J. (2008). Forecasting international bandwidth capacity using linear and ANN methods. Applied Economics, 40, 1775-1787. Hyndman, R.J., O'Hara-Wild, M., Bergmeir, C., and Razbash, S. (2017). Package 'forecast', September 25, 2017. Gardner Jr., E.S. (2006). Exponential smoothing: The state of the art - Part II. International Journal of Forecasting, 22, 637-666. Gardner Jr., E. S., and McKenzie, E. (1989). Seasonal exponential smoothing with damped trends. Management Science, 35, 372-376. Hyndman, R.J., and Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 26. Assimakopoulos, V., and Nikolopoulos, K. (2000). The theta model: a decomposition approach to forecasting. International Journal of Forecasting, 16, 521-530. Hyndman, R.J., and Billah, B. (2003). Unmasking the theta method. International Journal of Forecasting, 19, 287-290. Timmermann, A. (2006). Forecast combinations. Elliott, G., Granger, C.W.J., Timmermann, A. (Eds.), Handbook of Economic Forecasting, 135-196, Elsevier Press. Stock, J.H., and Watson, M.W. (2001). A comparison of linear and nonlinear univariate models for forecasting macroeconomic time series. In Engle, R.F., White, H. (Eds.) Festschrift in Honour of Clive Granger. Cambridge University press, Cambridge, 1-44. Bergmeir, C., Hyndman, R.J., and Benitez, J. M. (2016). Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation. International Journal of Forecasting, 32, 303-312. Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123-140. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/92727 |