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Constructing a cryptocurrency-price prediction model using deep learning

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dc.title Constructing a cryptocurrency-price prediction model using deep learning en
dc.contributor.author Tran, Toai Kim
dc.contributor.author Le, Thanh Thi Tuyet
dc.contributor.author Bui, Thinh Tien
dc.contributor.author Dang, Vang Quang
dc.contributor.author Šenkeřík, Roman
dc.relation.ispartof 8th International Conference on Engineering and Emerging Technologies, ICEET 2022
dc.identifier.issn 2831-3682 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.issn 2409-2983 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-1-6654-9106-8
dc.date.issued 2022
dc.event.title 8th International Conference on Engineering and Emerging Technologies, ICEET 2022
dc.event.location Kuala Lumpur
utb.event.state-en Malaysia
utb.event.state-cs Malajsie
dc.event.sdate 2022-10-27
dc.event.edate 2022-10-28
dc.type conferenceObject
dc.language.iso en
dc.publisher Institute of Electrical and Electronics Engineers Inc.
dc.identifier.doi 10.1109/ICEET56468.2022.10007138
dc.relation.uri https://ieeexplore.ieee.org/document/10007138
dc.relation.uri https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10007138
dc.subject cryptocurrency en
dc.subject deep learning en
dc.subject machine learning en
dc.description.abstract The purpose of this study is to discover the optimal Deep Learning model for Bitcoin prediction among the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our empirical results indicate that LSTM is the optimal model for predicting Bitcoin price and trend with the prediction accuracy of 88.9%. Our study serves as a stepping stone for novice cryptocurrency investors and future studies of more advanced and sophisticated algorithms. Finally, given that the ideal model for predicting the price of cryptocurrencies is still a topic of controversy, the findings of this study will serve as a valuable empirical resource for future studies. © 2022 IEEE. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1011371
utb.identifier.obdid 43884456
utb.identifier.scopus 2-s2.0-85146886476
utb.source d-scopus
dc.date.accessioned 2023-02-17T00:08:30Z
dc.date.available 2023-02-17T00:08:30Z
utb.contributor.internalauthor Šenkeřík, Roman
utb.fulltext.sponsorship -
utb.scopus.affiliation Ho Chi Minh City University of Technology and Education, Faculty of Economics, No 1 Vo Van Ngan Street, Linh Chieu Ward, Thu Duc City, Viet Nam; VSB-Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, Dept. of Computer Science, 17. Listopadu 2172/15, Ostrava-Poruba, 708 00, Czech Republic; Tomas Bata University in Zlin, Faculty of Applied Informatics, T. G. Masaryka 5555, Zlin, 76001, Czech Republic
utb.fulltext.projects -
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