Cell traffic prediction using joint spatio-temporal information

In future cellular networks, the ability to predict network parameters such as cell load will be a key enabler of several proposed adaptation and resource allocation techniques. In this study, we consider a joint exploitation of spatio-temporal data to improve the prediction accuracy of standard regression methods. We test several such methods from the literature on a publicly available dataset and document the advantages of the proposed approach.