A Computational Model for Predicting Cryptocurrencies Using Exogenous Variables

Eduardo Gonçalves, Eduardo Nunes Borges, Bruno Dalmazo, Rafael Alceste Berri, Giancarlo Lucca, Vinicius M. de Oliveira

2023

Abstract

The recent growth of cryptocurrencies caused worldwide interest due to capitalization power and geographic expansion. In this universe, Bitcoin is the main actor. Taking this into consideration, this paper aims to analyze the behavior of Bitcoin during the time. To do so, we use techniques already studied in the literature to perform the predictions and comparisons between methods jointly with exogenous variables to boost the results. An evaluation has been performed and the best results were achieved using the Long-Short-Term-Memory (LSTM) neural network model. Also, the experiments were carried out in different scenarios, using datasets with more than five years of daily records and exogenous variables to improve the performance of the models.

Paper Citation

in Harvard Style

Gonçalves E., Nunes Borges E., Dalmazo B., Alceste Berri R., Lucca G. and M. de Oliveira V. (2023). A Computational Model for Predicting Cryptocurrencies Using Exogenous Variables. In Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-648-4, SciTePress, pages 180-186. DOI: 10.5220/0011852000003467

in Bibtex Style

@conference{iceis23,
author={Eduardo Gonçalves and Eduardo Nunes Borges and Bruno Dalmazo and Rafael Alceste Berri and Giancarlo Lucca and Vinicius M. de Oliveira},
title={A Computational Model for Predicting Cryptocurrencies Using Exogenous Variables},
booktitle={Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2023},
pages={180-186},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011852000003467},
isbn={978-989-758-648-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - A Computational Model for Predicting Cryptocurrencies Using Exogenous Variables
SN - 978-989-758-648-4
AU - Gonçalves E.
AU - Nunes Borges E.
AU - Dalmazo B.
AU - Alceste Berri R.
AU - Lucca G.
AU - M. de Oliveira V.
PY - 2023
SP - 180
EP - 186
DO - 10.5220/0011852000003467
PB - SciTePress