As raw data become available in ever-increasing amounts, there is a need for automated methods that extract comprehensible knowledge from the data. In our previous work we have applied evolutionary algorithms to the problem of mining predictive rules from time series. In this paper we investigate the effect of discretization on the predictive power of the evolved rules. We compare the effects of using simple model selection based on validation performance, majority vote ensembles, and naive Bayesian combination of classifiers