In this article we are presenting an approach for fuzzy aggregation in ensembles of neural networks for forecasting. The aggregator in an ensemble is used to combine the outputs of the networks forming the ensemble, in such a way that the total output is better than the outputs of the individual modules. In our approach a fuzzy system is used to estimate the weights that will be assigned to the outputs in the process of combining them in a weighted average calculation. The uncertainty in the process of aggregation is modeled with interval type-3 fuzzy, which in theory can outperform type-2 and type-1. Results of the Dow Jones time series show the potential of the approach to outperform other aggregators.