Nonlinear time series analysis
Nonlinear time series analysis
Dynamics from multivariate time series
Physica D
Bluetooth and WAP push based location-aware mobile advertising system
Proceedings of the 2nd international conference on Mobile systems, applications, and services
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Prediction of multivariate chaotic time series with local polynomial fitting
Computers & Mathematics with Applications
NextPlace: a spatio-temporal prediction framework for pervasive systems
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
From big smartphone data to worldwide research: The Mobile Data Challenge
Pervasive and Mobile Computing
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Previous studies have shown that human movement is predictable to a certain extent at different geographic scales. The 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.