Mobile network traffic forecasting is a fundamental building block for key management tasks such as resources allocation. In particular, being able to predict traffic volume peaks is of primary importance for a correct network operation. This paper proposes to exploit exogenous inputs to predict such peaks, focusing in particular on football matches. We show with an analysis conducted on 4 of the major cities in Italy for a period of 6 months that volume traffic peaks are strongly correlated with the occurrence of football matches between specific teams and we propose a methodology to exploit the football calendar to forecast traffic peaks. The proposed forecasting frameworks allows to predict more than 50 % of the peaks, improving the forecasting performance compared to a traffic signature based approach and being able to forecast the maximum traffic volume with an average overestimation below +10% of the actual value.

(2022). Using the (Crystal) Ball: Forecasting Network Traffic Peaks with Football Events . Retrieved from https://hdl.handle.net/10446/263896

Using the (Crystal) Ball: Forecasting Network Traffic Peaks with Football Events

Pimpinella, Andrea;
2022-01-01

Abstract

Mobile network traffic forecasting is a fundamental building block for key management tasks such as resources allocation. In particular, being able to predict traffic volume peaks is of primary importance for a correct network operation. This paper proposes to exploit exogenous inputs to predict such peaks, focusing in particular on football matches. We show with an analysis conducted on 4 of the major cities in Italy for a period of 6 months that volume traffic peaks are strongly correlated with the occurrence of football matches between specific teams and we propose a methodology to exploit the football calendar to forecast traffic peaks. The proposed forecasting frameworks allows to predict more than 50 % of the peaks, improving the forecasting performance compared to a traffic signature based approach and being able to forecast the maximum traffic volume with an average overestimation below +10% of the actual value.
2022
Pimpinella, Andrea; Redondi, Alessandro E. C.; Pavon, Andrea; Venturini, Luisa
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/263896
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