A refined method of forecasting based on high-order intuitionistic fuzzy time series data

In this paper, we present a refined method of forecasting based on high-order intuitionistic fuzzy time series by transformed a historical fuzzy time series data into intuitionistic fuzzy time series data via defining their appropriate membership and non-membership function. The fuzzification of historical time series data is intuitionistic fuzzification which is based on their score and accuracy function. Also intuitionistic fuzzy logical relationship groups are defined and introduced a defuzzification process for high-order intuitionistic fuzzy time series. The aim of this paper is to propose an idea of high-order intuitionistic fuzzy time series which is generalization of fuzzy time series models and its experimental result shows that the proposed high-order intuitionistic fuzzy forecasting method gets better forecasting accuracy rates over the existing methods. The proposed method has been implemented on the historical enrollment data at the University of Alabama. The comparison result of these illustration shows that the proposed method has smaller forecasting accuracy rates in terms of MSE and MAPE over than the existing models in fuzzy time series. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.