Analysis of branch prediction via data compression
Proceedings of the seventh international conference on Architectural support for programming languages and operating systems
LeZi-update: an information-theoretic framework for personal mobility tracking in PCS networks
Wireless Networks - Selected Papers from Mobicom'99
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Personal and Ubiquitous Computing
Prediction of indoor movements using bayesian networks
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
Next place prediction using mobility Markov chains
Proceedings of the First Workshop on Measurement, Privacy, and Mobility
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Next location prediction anticipates a person's movement based on the history of previous sojourns. It is useful for proactive actions taken to assist the person in an ubiquitous environment. This paper evaluates next location prediction methods: dynamic Bayesian network, multi-layer perceptron, Elman net, Markov predictor, and state predictor. For the Markov and state predictor we use additionally an optimization, the confidence counter. The criterions for the comparison are the prediction accuracy, the quantity of useful predictions, the stability, the learning, the relearning, the memory and computing costs, the modelling costs, the expandability, and the ability to predict the time of entering the next location. For evaluation we use the same benchmarks containing movement sequences of real persons within an office building.