Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
A data mining approach for location prediction in mobile environments
Data & Knowledge Engineering
Extracting places from traces of locations
ACM SIGMOBILE Mobile Computing and Communications Review
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Understanding mobility based on GPS data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
WhereNext: a location predictor on trajectory pattern mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
PERCOM '11 Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications
NextPlace: a spatio-temporal prediction framework for pervasive systems
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
The Impact of Temporal Factors on Mobility Patterns
HICSS '12 Proceedings of the 2012 45th Hawaii International Conference on System Sciences
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
User location forecasting at points of interest
Proceedings of the 2012 RecSys workshop on Personalizing the local mobile experience
Predicting future locations with hidden Markov models
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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User location forecasting is central to establish context in proactive mobile applications. Knowing where the user will be at a given time enables standby action triggers ahead of time. User location exhibits periodic patterns grouped by time of day, day of the week, month of the year, etc. This characteristic has been exploited to model user location as a Markov process with great accuracy. Using yearly data from public sources it was possible to predict user location in a time frame of 8 hours with accuracy of up to 69%. One assumption of the above modeling is that user location is stationary in time. However, it is more natural to assume user location patterns may vary over time. For example one user may change job, or the relationship status, and avoid certain places frecuented in the past. In this paper we propose a learning mechanism adapting user location forecasting to behavior changes over time. Our model is able to predict for up to 94 weeks with 43% of accuracy.