Predictive Analysis of the Cryptocurrencies’ Movement Direction Using Machine Learning Methods

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dc.authorscopusid57205616621
dc.authorscopusid57205579603
dc.authorscopusid57225966836
dc.authorscopusid57205585353
dc.authorscopusid56557054300
dc.contributor.authorTimuçin, T.
dc.contributor.authorBayiroğlu, H.
dc.contributor.authorGündüz, H.
dc.contributor.authorYildiz, T. K.
dc.contributor.authorAtagün, E.
dc.date.accessioned2021-12-01T18:38:56Z
dc.date.available2021-12-01T18:38:56Z
dc.date.issued2021
dc.department[Belirlenecek]en_US
dc.description.abstractCryptocurrencies are among the most interesting financial instruments of recent years. Unlike the classical understanding that money exists as a means of change from one hand to another, this digital economy has begun to attract people's attention. The most popular currency emerging from this concept of cryptocurrency is “Bitcoin”. As the popularity of Bitcoin started to increase since 2016, the number of academic studies on cryptocurrencies has increased in parallel. In light of these developments, our study proposes predictive models of price change directions of high market value cryptocurrencies. Bitcoin, Ethereum and Litecoin were selected as cryptocurrencies and daily opening, closing, high and low prices of these currencies were collected from financial websites. Preprocessing was performed on the collected data to create input vectors. These vectors were given regression algorithms which are Multiple Linear, Polynomial, Support Vector, Decision Tree and Random Forest Regression. As evaluation metrics, R-square Method (R2) and Root Mean Square Error (RMSE) were chosen. After doing experiments with different parameter settings, it was found out that the chosen machine learning models showed satisfactory performances in predicting the directions of then mentioned cryptocurrencies. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.identifier.doi10.1007/978-3-030-79357-9_26
dc.identifier.endpage264en_US
dc.identifier.issn23674512
dc.identifier.scopus2-s2.0-85109893289en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage256en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-79357-9_26
dc.identifier.urihttps://hdl.handle.net/20.500.12684/9923
dc.identifier.volume76en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes on Data Engineering and Communications Technologiesen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCryptocurrencyen_US
dc.subjectData miningen_US
dc.subjectPredictionen_US
dc.subjectRegressionen_US
dc.titlePredictive Analysis of the Cryptocurrencies’ Movement Direction Using Machine Learning Methodsen_US
dc.typeBook Chapteren_US

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