Akademik Veri Yönetim Sistemi
Fuzzy Sets and Systems, cilt.527, 2026 (SCI-Expanded, Scopus)
Type-1 Fuzzy Functions (T1FFs) have emerged as a rule-free alternative to classical fuzzy inference systems for tackling forecasting problems, especially in the presence of uncertainty and nonlinearity. This study provides a comprehensive literature review and a bibliometric analysis of T1FFs in the context of forecasting. A dataset of 25 articles indexed in the Web of Science Core Collection was examined to assess research trends, author collaboration networks, influential publications, source journals, and key thematic areas. Our review identifies four main components in the design of T1FF-based models: input structure, clustering methods, forecasting models, and objective function optimization. Through citation and co-authorship network analysis, we highlight prominent researchers and collaborations within the field. Source journal analysis reveals publication hotspots, while co-word analysis identifies dominant themes such as “forecasting,” “robust regression,” and “metaheuristic optimization.” The results suggest growing academic interest and methodological diversification in the use of T1FFs, with Turkey leading international contributions. This study serves as a roadmap for researchers aiming to build or extend T1FF-based forecasting systems.