Forecasting Bitcoin Price by Tuned Long Short Term Memory Model

Aleksandar Petrovic; Luka Jovanovic; Miodrag Zivkovic; Nebojsa Bacanin; Nebojsa Budimirovic; Marina Marjanovic

doi:10.2991/978-94-6463-110-4_14

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Forecasting Bitcoin Price by Tuned Long Short Term Memory Model

Authors

Aleksandar Petrovic¹, Luka Jovanovic¹, Miodrag Zivkovic¹, Nebojsa Bacanin¹^{, *}, Nebojsa Budimirovic¹, Marina Marjanovic¹

¹Singidunum University, Danijelova 32, 11000, Belgrade, Serbia

^*Corresponding author. Email: nbacanin@singidunum.ac.rs

Corresponding Author

Nebojsa Bacanin

Available Online 30 January 2023.

DOI: 10.2991/978-94-6463-110-4_14 How to use a DOI?
Keywords: cryptocurrency; prediction; arithmetic optimization algorithm; long short term memory model; optimization
Abstract: The interest for cryptocurrencies is high and hence this work focuses on providing a practical real-world application of the swarm metaheuristics and long short term memory model (LSTM). The goal is price forecasting which is interesting due to the high volatility of the cryptocurrencies. The authors apply LSTM for the solution of the problem which has been proven to reap results with this type of problem. The LSTM is further optimized by a swarm metaheuristic - arithmetic optimization algorithm (AOA). The solution was tested alongside familiar high-performing competitors with the use of standard metrics mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). These metrics have been used for comparison between the solutions, upon which the proposed solution obtained overall best performance that testifies to the improvement of the solution.
Copyright: © 2023 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)
Series: Advances in Computer Science Research
Publication Date: 30 January 2023
ISBN: 10.2991/978-94-6463-110-4_14
ISSN: 2352-538X
DOI: 10.2991/978-94-6463-110-4_14 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Aleksandar Petrovic
AU  - Luka Jovanovic
AU  - Miodrag Zivkovic
AU  - Nebojsa Bacanin
AU  - Nebojsa Budimirovic
AU  - Marina Marjanovic
PY  - 2023
DA  - 2023/01/30
TI  - Forecasting Bitcoin Price by Tuned Long Short Term Memory Model
BT  - Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)
PB  - Atlantis Press
SP  - 187
EP  - 202
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-110-4_14
DO  - 10.2991/978-94-6463-110-4_14
ID  - Petrovic2023
ER  -

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