@inproceedings{krumm2011learning,
author = {Krumm, John and Brush, A.J.},
title = {Learning Time-Based Presence Probabilities},
booktitle = {Pervasive 2011},
year = {2011},
month = {June},
abstract = {Many potential pervasive computing applications could use predictions of when a person will be at a certain place. Using a survey and GPS data from 34 participants in 11 households, we develop and test algorithms for predicting when a person will be at home or away. We show that our participants’ self-reported home/away schedules are not very accurate, and we introduce a probabilistic home/away schedule computed from observed GPS data. The computation includes smoothing and a soft schedule template. We show how the probabilistic schedule outperforms both the self-reported schedule and an algorithm based on driving time. We also show how to combine our algorithm with the best part of the drive time algorithm for a slight boost in performance.},
publisher = {Springer Verlag},
url = {http://approjects.co.za/?big=en-us/research/publication/learning-time-based-presence-probabilities/},
edition = {Pervasive 2011},
}