A new approach for time series prediction using ensembles of ANFIS models

This paper describes an architecture for ensembles of ANFIS (adaptive network based fuzzy inference 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 we are considered are: the Mackey–Glass, Dow Jones and Mexican stock exchange. The methods used for the integration of the ensembles of ANFIS are: integrator by average and the integrator by weighted average. 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.