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Network slicing is increasingly used to partition network infrastructurebetween different mobile services. Precise service-wise mobile trafficforecasting becomes essential in this context, as mobile operators seek topre-allocate resources to each slice in advance, to meet the distinctrequirements of individual services. This paper attacks the problem ofmulti-service mobile traffic forecasting using a sequence-to-sequence (S2S)learning paradigm and convolutional long short-term memories (ConvLSTMs). Theproposed architecture is designed so as to effectively extract complexspatiotemporal features of mobile network traffic and predict with highaccuracy the future demands for individual services at city scale. We conductexperiments on a mobile traffic dataset collected in a large Europeanmetropolis, demonstrating that the proposed S2S-ConvLSTM can forecast themobile traffic volume produced by tens of different services in advance of upto one hour, by just using measurements taken during the past hour. Inparticular, our solution achieves mean absolute errors (MAE) at antenna levelthat are below 13KBps, outperforming other deep learning approaches by up to31.2%.

Multi-Service Mobile Traffic Forecasting via Convolutional Long Short-Term Memories