Learning and user adaptation in location forecasting

Abstract

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%.

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