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Objective: Prediction of moving objects with uncertain motion patterns is emerging rapidly as a new exciting paradigm and is important for law enforcement applications such as criminal tracking analysis. However, existing algorithms for prediction in spatio-temporal databases focus on discovering frequent trajectory patterns from historical data. Moreover, these methods overlook the effect of some important factors, such as speed and moving direction. This lacks generality as moving objects may follow dynamic motion patterns in real life.Methods: We propose a framework for predicating uncertain trajectories in moving objects databases. Based on Continuous Time Bayesian Networks (CTBNs), we develop a trajectory prediction algorithm, called PutMode (Prediction of uncertain trajectories in Moving objects databases). It comprises three phases: (i) construction of TCTBNs (Tra-S. Qiao ( ) School jectory CTBNs) which obey the Markov property and consist of states combined by three important variables including street identifier, speed, and direction; (ii) trajectory clustering for clearing up outlying trajectories; (iii) predicting the motion behaviors of moving objects in order to obtain the possible trajectories based on TCTBNs.Results: Experimental results show that PutMode can predict the possible motion curves of objects in an accurate and efficient manner in distinct trajectory data sets with an average accuracy higher than 80%. Furthermore, we illustrate the crucial role of trajectory clustering, which provides benefits on prediction time as well as prediction accuracy.

Abstract. Two novel methods for Time Series Prediction based on GEP (Gene Expression Programming (5) It is proved that a minimum expression discovered by GEP-MC method with error not exceeding δ/2 uses at most log 3 (2L/δ) operators and the problem to find δ-accurate expression with fewer operators is NP-hard. Extensive experiments on real data sets for sun spot prediction show that the performance of the new method is 20-900 times higher than existing algorithms.