Dual Hesitant Fuzzy Set-Based Intuitionistic Fuzzy Time Series Forecasting

In the present study, we have introduced application of dual hesitant fuzzy set (DHFS) in intuitionistic fuzzy time series forecasting to handle non-determinism that occurs due to multiple valid fuzzification method for time series data using both membership and nonmembership values. The shortcomings of intuitionistic fuzzy sets are only able to handle the non-determinism and hesitancy corresponding to single membership grade, while hesitant fuzzy sets are not capable of epistemic uncertainty degree of an element. These shortcomings are very effectively tackled by DHFS due to its multifold ways tool to handle non-determinism and hesitancy in the system. In the present study, we have used mean-based discretization (MBD) approach to partition the universe of discourse and two different fuzzification methods (triangular and Gaussian) to construct DHFS. Further, elements of DHFS are aggregated into an intuitionistic fuzzy set using an aggregation operator. Proposed method is implemented over the historical enrollments data of Alabama University to confirm its outperformance over few existing time series forecasting method using RMSE and AFER.