期刊
PERVASIVE AND MOBILE COMPUTING
卷 9, 期 6, 页码 798-807出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.pmcj.2013.07.008
关键词
Mobility prediction; Mutual information; Nonlinear time series analysis
资金
- EPSRC [EP/J005266/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/J005266/1] Funding Source: researchfish
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. (C) 2013 Elsevier B.V. All rights reserved.
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