Spatio-Temporal Ensemble Prediction on Mobile Broadband Network Data

Facing the huge success of mobile devices, network
providers ceaselessly deploy new nodes (cells) to always guarantee
a high quality of service. Nevertheless, keeping turned on all the
nodes when traffic is low is energy inefficient. This has led to
investigations on the possibility to turn off network nodes, fully
or partly, in low traffic loads. To accomplish such a dynamic
network optimization, it is crucial to predict very accurately
low traffic periods. In this paper, we tackle this problem using
data mining and propose Spatio-Temporal Ensemble Prediction
(STEP). In a nutshell, STEP is based on the following two main
ideas: (1) since traffic shows very different behaviors depending
on both the temporal and the spatial contexts, several prediction
models are built to fit these characteristics; (2) we propose an
ensemble prediction technique that accurately predicts low traffic
periods. We empirically show on a real dataset that our approach
outperforms standard methods on the low traffic prediction task