Evolutionary Interval Type-۲ Fuzzy Rule Learning Approaches for Uncertain Time-Series Prediction

This study presents Interval Type-۲ Fuzzy Evolutionary models to manage uncertainty in the process of uncertain time-series prediction. This study presents two type-۲ fuzzy evolutionary models for rule extraction that were proposed: ۱) Evolutionary Interval Type-۲ Fuzzy Rule Learning (EIT۲FRL), and ۱) Evolutionary Interval Type-۲ Fuzzy Rule-Set Learning (EIT۲FRLS). A ROC curve analysis was applied for performance evaluation, and the results were validated using a ۱۰-fold cross-validation technique. The results reveal that the proposed methods have an AUC of ۰.۹۶ for EIT۲FRLS and ۰.۹۳ for EIT۲FRL proposed methods. The results are promising for knowledge extraction in uncertain circumstances, predicting uncertain patterns prediction, and making suitable strategies and optimal decisions.

Keywords:

Evolutionary Algorithm , Type-۲ Fuzzy Logic , Time-Series Prediction