Time series prediction based on ensemble ANFIS

In this paper, random and bootstrap sampling method and ANFIS (adaptive network based fuzzy inference system) are integrated into En-ANFIS (an ensemble ANFIS) to predict chaotic and traffic flow time series. The prediction results of En-ANFIS are compared with an ANFIS using all training data and each ANFIS unit in En-ANFIS. Experimental results show that the prediction accuracy of the En-ANFIS is higher than that of single ANFIS unit, while the number of training sample and training time of the En-ANFIS are less than that of the ANFIS using all training data. So, En-ANFIS is an effective method to achieve both high accuracy and less computational complexity for time series prediction.