In this work, we proposed to use the Zoomed Ranking approach to rank and select time series models. Zoomed Ranking, originally proposed to generate a ranking of candidate algorithms, is employed to solve a given classification problem based on performance information from previous problems. The problem of model selection in Zoomed Ranking was solved in two distinct phases. In the first phase, we selected a subset of problems from the instances base that were similar to the new problem at hand. This selection is made using the k-Nearest Neighbor algorithm, whose distance function uses the characteristics of the series. In the second phase, the ranking of candidate models was generated based on performance information (accuracy and execution time) of the models in the series selected from the previous phase. Our experiments using the Zoomed Ranking revealed encouraging results.