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JANKOVÁ, Z. RAKOVSKÁ, E.
Original Title
Comparison Uncertainty of Different Types of Membership Functions in T2FLS: Case of International Financial Market
Type
book chapter
Language
English
Original Abstract
This article deals with the determination and comparison of different types of functions of the type-2 interval of fuzzy logic, using a case study on the international financial market. The model is demonstrated on the time series of the leading stock index DJIA of the US market. Type-2 Fuzzy Logic membership features are able to include additional uncertainty resulting from unclear, uncertain or inaccurate financial data that are selected as inputs to the model. Data on the financial situation of companies are prone to inaccuracies or incomplete information, which is why the type-2 fuzzy logic application is most suitable for this type of financial analysis. This paper is primarily focused on comparing and evaluating the performance of different types of type-2 fuzzy membership functions with integrated additional uncertainty. For this purpose, several model situations differing in shape and level or degree of uncertainty of membership functions are constructed. The results of this research show that type-2 fuzzy sets with dual membership functions is a suitable expert system for highly chaotic and unstable international stock markets and achieves higher accuracy with the integration of a certain level of uncertainty compared to type-1 fuzzy logic.
Keywords
computational finance; fuzzy logic; membership function; Type-1 fuzzy sets; T1FLS; Type-2 fuzzy sets; T2FLS
Authors
JANKOVÁ, Z.; RAKOVSKÁ, E.
Released
21. 11. 2022
Publisher
MDPI
Location
Basel, Switzerland
ISBN
978-3-0365-5532-4
Book
Women in Artificial Intelligence (AI)
Edition
1
Edition number
Pages from
279
Pages to
299
Pages count
20
BibTex
@inbook{BUT180005, author="Zuzana {Janková} and Eva {Rakovská}", title="Comparison Uncertainty of Different Types of Membership Functions in T2FLS: Case of International Financial Market", booktitle="Women in Artificial Intelligence (AI)", year="2022", publisher="MDPI", address="Basel, Switzerland", series="1", edition="1", pages="279--299", isbn="978-3-0365-5532-4" }