10.30495/SPRE.2023.1055991

Evolutionary Interval Type-2 Fuzzy Rule Learning Approaches for Uncertain Time-Series Prediction

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  • Aref Safari*1,
  • Rahil Hosseini1,
  1. Department of Computer Engineering, Islamic Azad University, Shahr-e-Qods Branch, Tehran, Iran

Revised: 2022-07-03

Accepted: 2022-10-12

Published in Issue 2023-03-01

How to Cite

Safari, A., & Hosseini, R. (2023). Evolutionary Interval Type-2 Fuzzy Rule Learning Approaches for Uncertain Time-Series Prediction. Signal Processing and Renewable Energy (SPRE), 7(1), 27-39. https://doi.org/10.30495/SPRE.2023.1055991
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Abstract

This study presents Interval Type-2 Fuzzy Evolutionary models to manage uncertainty in the process of uncertain time-series prediction. This study presents two type-2 fuzzy evolutionary models for rule extraction that were proposed: 1) Evolutionary Interval Type-2 Fuzzy Rule Learning (EIT2FRL), and 1) Evolutionary Interval Type-2 Fuzzy Rule-Set Learning (EIT2FRLS). A ROC curve analysis was applied for performance evaluation, and the results were validated using a 10-fold cross-validation technique. The results reveal that the proposed methods have an AUC of 0.96 for EIT2FRLS and 0.93 for EIT2FRL proposed methods. The results are promising for knowledge extraction in uncertain circumstances, predicting uncertain patterns prediction, and making suitable strategies and optimal decisions.
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