By definition, the time series represents the behavior of a variable as a time function. For the series forecasting process, the model must be able to learn the temporal dynamics of the variables in order to obtain consistent future values. However, an accurate time series prediction is a task that goes beyond choosing the most complex (or promising) model that is applicable to the type of problem, and therefore the analysis step is a fundamental procedure to guide the adaptation of a model. Specifically, in multivariate problems, AutoMFIS is a model based on fuzzy logic, developed not only to give accurate forecasts but also to introduce the explainability of results through semantically understandable rules. Even with such promising characteristics, this system has shown practical limitations in problems that involve datasets of high dimensionality. With the increasing demand formethods to deal with large datasets, it should be great that approaches for the automatic synthesis of fuzzy systems could be adapted to cover a new class of forecasting problems. This dissertation proposes an extension of the base model AutoMFIS modeling method for time series forecasting with high dimensionality data, named as e-AutoMFIS. Based on the Ensemble learning theory, this new methodology applies distributed learning to generate fuzzy rules. The main characteristics of the proposed model are described, highlighting the changes in order to improve both the accuracy and the interpretability of the system. The proposed model is also evaluated in different case studies, in which the results are compared in terms of accuracy against the results produced by other methods in the literature. In addition, in each selected problem, the aspect of interpretability is also assessed, which is essential for explainability evaluation.