electronics Article Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series Carolina Gijón * , Matías Toril , Salvador Luna-Ramírez , María Luisa Marí-Altozano and José María Ruiz-Avilés Instituto de Telecomunicaciones (TELMA), Universidad de Málaga, CEI Andalucía TECH, 29071 Málaga, Spain;
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[email protected] (S.L.-R.);
[email protected] (M.L.M.-A.);
[email protected] (J.M.R.-A.) * Correspondence:
[email protected] Abstract: Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottle- necks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (sec- onds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Citation: Gijón, C.; Toril, M.; Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average Luna-Ramírez, S.; Marí-Altozano, and Additive Holt–Winters.