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When users have to choose a learning algorithm to induce a model for a given dataset, a common practice is to select an algorithm whose bias suits the data distribution. In real-world applications that produce data continuously this distribution may change over time. Thus, a learning algorithm with the adequate bias for a dataset may become unsuitable for new data following a different distribution. In this paper we present a meta-learning approach for periodic algorithm selection when data distribution may change over time. This approach exploits the knowledge obtained from the induction of models for different data chunks to improve the general predictive performance. It periodically applies a meta-classifier to predict the most appropriate learning algorithm for new unlabeled data. Characteristics extracted from past and incoming data, together with the predictive performance from different models, constitute the meta-data, which is used to induce this meta-classifier. Experimental results using data of a travel time prediction problem show its ability to improve the general performance of the learning system. The proposed approach can be applied to other time-changing tasks, since it is domain independent.