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Time series clustering provides underpinning techniques for discovering the intrinsic structure and condensing/summarizing information conveyed in time series, which is demanded in various fields ranging from bioinformatics to video content understanding. In this paper, we present an unsupervised ensemble learning approach to time series clustering by combining rival-penalized competitive learning (RPCL) networks with different representations of time series. In our approach, the RPCL network ensemble is employed for clustering analyses based on different representations of time series whenever available, and an optimal selection function is applied to find out a final consensus partition from multiple partition candidates yielded by applying various consensus functions for the combination of competitive learning results. As a result, our approach first exploits its capability of the RPCL rule in clustering analysis of automatic model selection on individual representations and subsequently applies ensemble learning for the synergy of reconciling diverse partitions resulted from the use of different representations and augmenting RPCL networks in automatic model selection and overcoming its inherent limitation. Our approach has been evaluated on 16 benchmark time series data mining tasks with comparison to state-of-the-art time series clustering techniques. Simulation results demonstrate that our approach yields favorite results in clustering analysis of automatic model selection.