More than a decade of research has produced numerous representations and similarity measures to support time series classification and clustering. Yet most of the work in the field is so focused on the representation or similarity measure that it ignores the possibility of improving performance using ensembles of representations or classifiers. This paper explores ways of exploiting representational diversity for time series classification via ensembles of representations. We focus on the Symbolic Aggregate approXimation (SAX) discretization method coupled with the bag-of-patterns (BoP) representation because of their state-of-the-art performance in the single representation/classifier case. Experiments with a number of standard benchmark time series datasets and a new dataset of vital signs collected from patients suffering from traumatic brain injury demonstrate the power of the ensemble approaches. The result is a single method that is often significantly better than vanilla SAX/BoP and compares favorably on a per dataset basis with the best methods reported in the literature for each dataset.