Time series combined forecasters have been superior to the respective single models in statistical terms. In this way, the linear combination functions, e.g. the simple average (SA) and the minimal variance (MV) approaches, have been the main alternatives for aggregation in the literature. In this work, it is proposed a copulas-based method for combining biased single models. Copulas are multivariate functions that operate on marginal probability distributions, allowing one to model the forecasters errors and then the dependence among them: a typical divide-and-conquer framework that can result in nonlinear accurate combined forecasters. The performance of the copulas-based combination method is assessed by means of a comparison with SA and MV models, based on two financial time series.