Copulas-based time series combined forecasters

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 presented a copulas-based method for combining biased single models. Copulas are multivariate functions that operate on marginal probability distributions and have the specific advantage of generalizing MV by flexibly modelling the forecasters residuals and then the dependence among them: a typical divide-and-conquer framework that can result in superior combined forecasters. The usefulness of the copulas-based combination method is highlighted by means of a comparison with SA and MV models, based on a number of simulated cases and a real-world time series.