A difficult aspect of a time dependent classification task is that the data are not IID sampled. To model this dependency several approaches in longitudinal analysis were developed. However, these approaches either have trouble estimating their generalization performance or are parametric in a statistical sense. To overcome these problems we propose in this paper a new approach of time dependent ensembles. Our approach decomposes the time dependent classification task into a series of classification tasks with IID sampled data. Each task can be solved by a classifier that is not supposed to model any dependency in the data. This allows for the use of a much broader spectrum of existing approaches than is possible on the original data. The classifiers associated with the tasks form the time dependent ensembles. The ensembles estimates the final class of the objects being classified by using a voting scheme. The experiments show the potential of the time dependent ensembles.