One of the main problems associated with the bagging technique in ensemble learning is its random sample selection in which all samples are treated with the same chance of being selected. However, in time-varying dynamic systems, the samples in the training set have not equal importance, where the recent samples contain more useful and accurate information than the former ones. To overcome this problem, this paper proposes a new time-based ensemble learning method, called temporal bagging (T-Bagging). The significant advantage of our method is that it assigns larger weights to more recent samples with respect to older ones, so it reduces the selection chances of former samples, and, thus, it addresses the adaptation to changes in dynamic systems. The experiments show that the proposed T-Bagging method improves the prediction accuracy of the model compared to the standard bagging method on temporal data.