Time series prediction using ensembles of neuro-fuzzy models with interval type-2 and type-1 fuzzy integrators

Abstract

This paper describes an architecture for Ensembles of Neuro-Fuzzy models with interval type-2 and type-1 fuzzy integrators, with emphasis on its application to the prediction of time series, where the objective is obtained the goal is to minimize the prediction error. The time series that was considered is the Mackey-Glass. The methods used for the integration of the ensembles of neuro-fuzzy (we used the ANFIS models "adaptive network based fuzzy inference system") are: integration by average, the integration by weighted average, interval type-2 and type-1 fuzzy inference systems (FIS) integrators. The performance obtained with this architecture overcomes several standard statistical approaches and neural network models reported in the literature by various researchers.

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