Neural networks, Bayesian networks, Markov models, and state predictors are different methods to predict the next location. For all methods a lot of parameters must be set up which differ for each user. Therefore a complex configuration must be made before such a method can be used. A hybrid predictor can reduce the configuration overhead utilizing different prediction methods or configurations in parallel to yield different prediction results. A selector chooses the most appropriate prediction result from the result set of the base predictors. We propose and evaluate three principal hybrid predictor approaches – the warm-up predictor, the majority predictor, and the confidence predictor – with several variants. The hybrid predictors reached a higher prediction accuracy than the average of the prediction accuracies of the separately used predictors.