Abstract
Fuzzy time series is a powerful tool to forecast the time series data under uncertainty. Fuzzy time series was first initiated with fuzzy sets and then generalized by intuitionistic fuzzy sets. The intuitionistic fuzzy sets consider the degree of hesitation in which the degree of non-membership is incorporated. In this paper, a fuzzy set time series forecasting model based on intuitionistic fuzzy sets via delegation of hesitancy degree to the major grade de-i-fuzzification approach was developed. The proposed model was implemented on the data of student enrollments at the University of Alabama. The forecasted output was obtained using the fuzzy logical relationships of the output, and the performance of the forecasted output was compared with the fuzzy time series forecasting model based on fuzzy sets using the mean square error, root mean square error, mean absolute error, and mean absolute percentage error. The results showed that the forecasting model based on induced fuzzy sets from intuitionistic fuzzy sets performs better compared to the fuzzy time series forecasting model based on fuzzy sets.
Highlights
Fuzzy time series was first introduced by Song and Chissom [1] and it was applied to the data of student enrollments at the University of Alabama [2]
A process of converting an intuitionistic fuzzy set into a where the functions μI (x) : D → [0,1] and fuzzy set is one of the core procedures in the fuzzy time series forecasting model based on the intuitionistic fuzzy set, which was termed as the de-i-fuzzification process by Attanassova [22] and Ban et al [23]
We present the forecasted enrollments using the proposed model and compare them with the fuzzy time series forecasting model based on fuzzy sets
Summary
Fuzzy time series was first introduced by Song and Chissom [1] and it was applied to the data of student enrollments at the University of Alabama [2]. A process of converting an intuitionistic fuzzy set into a where the functions μI (x) : D → [0,1] and fuzzy set is one of the core procedures in the fuzzy time series forecasting model based on the intuitionistic fuzzy set, which was termed as the de-i-fuzzification process by Attanassova [22] and Ban et al [23] During this process, ν I (x) : D → [0,1] are the degree of membership and non-membership of x in D, respectively. Ansari et al [24] proposed a de-i-fuzzification process by assigning the degree of hesitancy to the major grade of membership or non-membership. The proposed de-i-fuzzification process is given as follows: Let π be the degree of hesitancy and the intuitionistic fuzzy set is given as I = x, μ(x),ν (x).
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
