Forecasting Mobile Cellular Traffic Sampled at Different Frequencies

We consider the problem of forecasting high-frequency sampled mobile cellular traffic starting from a lower-frequency sampled time series. We use a dataset of real downlink/uplink traffic traces obtained from a mobile cellular network and apply different methodologies for performing forecasts at different sampling frequencies. Through extensive evaluation, we show that such type of forecasting is possible and in some cases is also able to outperform forecast results obtained starting directly from the high-frequency time series. The outcomes of this work can be used for several scenarios of cognitive networking, including prediction of data traffic requests in specific locations, as well as for data storage optimization and improvement of BBU clustering tasks.