Fuzzy time series forecasting method based on hesitant fuzzy sets

This study proposes a fuzzy time series forecasting method based on hesitant fuzzy sets for forecasting in the environment of hesitant information. The proposed method addresses the problem of establishing a common membership grade for the situation when multiple fuzzification methods are available to fuzzify time series data. An aggregation operator for aggregating hesitant information is also proposed in the study. The proposed method is implemented to forecast enrollment at University of Alabama and price of state bank of India (SBI) share at Bombay stock exchange (BSE), India. In both time series data are fuzzified with triangular fuzzy sets constructed using intervals of equal and unequal length. The performance of the proposed method in forecasting student enrollments and SBI share price is measured in terms of root mean square and average forecasting errors. Statistical validation and performance analysis is also carried out to validate the proposed forecasting method.