Constructing a cryptocurrency-price prediction model using deep learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F22%3A63561218" target="_blank" >RIV/70883521:28140/22:63561218 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/abstract/document/10007138/authors#authors" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10007138/authors#authors</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICEET56468.2022.10007138" target="_blank" >10.1109/ICEET56468.2022.10007138</a>
Alternative languages
Result language
angličtina
Original language name
Constructing a cryptocurrency-price prediction model using deep learning
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
8th International Conference on Engineering and Emerging Technologies, ICEET 2022
ISBN
978-1-66549-106-8
ISSN
2409-2983
e-ISSN
2831-3682
Number of pages
6
Pages from-to
1-6
Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
Piscataway, New Jersey
Event location
Kuala Lumpur
Event date
Oct 27, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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