An Improved Adaptive Neuro Fuzzy Inference for the Step Forward Forecasting of Time Series
R. Heshmati, Mohammad Javad Mahmoodabadi, A. Bagheri, Behnam Miripour Fard · Journal of advanced computing · 2013
In statistics, signal processing, and mathematical finance; a time series is a sequence of data points that measured at uniform time intervals. The prediction of time series is a very complicated process. In this paper, an improved Adaptive Neuro Fuzzy Inference System (ANFIS) is taken for predicting Mackey-Glass which is one of the chaotic time series. In the modeling of linear and stationary time series, it is necessary to choose the class of Auto Regressive Integrated Moving Average (ARIMA) models because of its high performance and robustness. Hence, in this paper, ANFIS becomes robust by ARIMA model. Simulation demonstrates that the proposed model has a good predictive capability.