  Previous studies have shown that human movement is predictable to a certain
extent at different geographic scales. Existing prediction techniques exploit
only the past history of the person taken into consideration as input of the
predictors. In this paper, we show that by means of multivariate nonlinear time
series prediction techniques it is possible to increase the forecasting
accuracy by considering movements of friends, people, or more in general
entities, with correlated mobility patterns (i.e., characterised by high mutual
information) as inputs. Finally, we evaluate the proposed techniques on the
Nokia Mobile Data Challenge and Cabspotting datasets.
