A new approach for time series prediction using ensembles of ANFIS models with interval type-2 and type-1 fuzzy integrators

Abstract

This paper describes an architecture for Ensembles of ANFIS (adaptive network based fuzzy inference system), with integrators of type-1 FLS and interval type-2 FLS (Fuzzy Logic System), with emphasis on its application to the prediction of chaotic time series, where 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 ANFIS are: Integration by average, the integration by weighted average, integration by type-1 FLS and integration by interval type-2 FLS. The performance obtained with this architecture overcomes several standard statistical approaches and neural network models reported in the literature by various researchers. In the experiments we changed the type of membership functions and the desired goal error, thereby increasing the complexity of the training.