Estimating Return Rate of Blockchain Financial Product by ANFIS-PSO Method

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dc.authoridOzturk Birim, Sule/0000-0001-7544-8588
dc.authoridEratas Sonmez, Filiz/0000-0003-2052-340X
dc.authoridSAGLAM, YAGMUR/0000-0001-6465-0297
dc.contributor.authorBirim, Sule Ozturk
dc.contributor.authorSonmez, Filiz Eratas
dc.contributor.authorLiman, Yagmur Saglam
dc.date.accessioned2025-03-23T19:47:05Z
dc.date.available2025-03-23T19:47:05Z
dc.date.issued2022
dc.departmentSinop Üniversitesi
dc.description4th International Conference on Intelligent and Fuzzy Systems (INFUS) -- JUL 19-21, 2022 -- Bornova, TURKEY
dc.description.abstractToday, blockchain technology is developing rapidly and the volume of blockchain financial product trading is increasing rapidly as well. The aim of this study is to predict the return rates of cryptocurrencies with the help of artificial learning applications, considering the complex and unstable structure of the financial system. The rate of return is one of the important criteria used for investment decisions. Therefore, an efficient method for return rate prediction will help investors in preparing their portfolios. Ethereum, one of the top three most traded cryptocurrencies in the world, was chosen for empirical analysis. The adaptive neuro-fuzzy inference system approach (ANFIS) has emerged as a method that has been frequently used in recent years. ANFIS uses optimization algorithms to obtain the best prediction performance based on neural network modeling. The ANFIS approach has a multilayered structure consisting of many nodes inside and connections between the layers. ANFIS retains the properties of a fuzzy system while applying the principles of a neural network. Computations in the layers are conducted to learn and reproduce the information of the system. In this study, the particle swarm optimization (PSO) algorithm is used to train the ANFIS network. PSO aims to find the best-performing model in predicting the prices of three major cryptocurrencies that are Bitcoin, Ethereum, and Tether. The prediction accuracy of the proposed models was checked on the test set with performance indicators of root mean squared error (RMSE) and mean absolute percentage error (MAPE). The ANFIS-PSO approach gives strong results in cryptocurrency rate of return estimation.
dc.identifier.doi10.1007/978-3-031-09173-5_92
dc.identifier.endpage809
dc.identifier.isbn978-3-031-09173-5
dc.identifier.isbn978-3-031-09172-8
dc.identifier.issn2367-3370
dc.identifier.issn2367-3389
dc.identifier.scopus2-s2.0-85135080900
dc.identifier.scopusqualityQ4
dc.identifier.startpage802
dc.identifier.urihttps://doi.org/10.1007/978-3-031-09173-5_92
dc.identifier.urihttps://hdl.handle.net/11486/7274
dc.identifier.volume504
dc.identifier.wosWOS:000889380800092
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer International Publishing Ag
dc.relation.ispartofIntelligent and Fuzzy Systems: Digital Acceleration and the New Normal, Infus 2022, Vol 1
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250323
dc.subjectANFIS
dc.subjectPSO
dc.subjectANFIS-PSO
dc.subjectCryptocurrencies
dc.subjectBitcoin
dc.subjectEthereum
dc.subjectTether
dc.titleEstimating Return Rate of Blockchain Financial Product by ANFIS-PSO Method
dc.typeConference Object

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