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Predicting Bitcoin and Ethereum prices using long short-term memory and gated recurrent unit

Predicting future prices of cryptocurrencies, including Bitcoin and Ethereum, presents a formidable challenge owing to their inherent volatility. This study applies Long Short-Term Memory (LSTM), a well-established recurrent neural network for time series forecasting, to predict Bitcoin and Ethereum...

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Main Authors: Mohd Haziq, Abdul Hadi, Nor Azuana, Ramli, Islam, Q. U. I.
Format: Article
Language:English
Published: Penerbit UMP 2023
Subjects:
QA Mathematics
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/41610/1/document.pdf
http://umpir.ump.edu.my/id/eprint/41610/
https://doi.org/10.15282/daam.v4i2.10195
https://doi.org/10.15282/daam.v4i2.10195
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spelling my.ump.umpir.416102024-06-20T02:07:11Z http://umpir.ump.edu.my/id/eprint/41610/ Predicting Bitcoin and Ethereum prices using long short-term memory and gated recurrent unit Mohd Haziq, Abdul Hadi Nor Azuana, Ramli Islam, Q. U. I. QA Mathematics QA75 Electronic computers. Computer science Predicting future prices of cryptocurrencies, including Bitcoin and Ethereum, presents a formidable challenge owing to their inherent volatility. This study applies Long Short-Term Memory (LSTM), a well-established recurrent neural network for time series forecasting, to predict Bitcoin and Ethereum values. Historical price data for both cryptocurrencies, sourced from Yahoo Finance, serves as the basis for analysis. The dataset undergoes an 80% training and 20% testing partition. Subsequently, LSTM models are developed and trained on both datasets. In parallel, the gated recurrent unit (GRU), recognized as an advanced variant of the LSTM model, is explored for comparative purposes. Performance evaluation utilizes fundamental metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results reveal an intriguing trend: both models exhibit superior performance when applied to the Ethereum dataset compared to the Bitcoin dataset. This observation suggests the potential presence of Ethereum-specific features or patterns that align more effectively with deep learning model architectures. Notably, the GRU model consistently outperforms the LSTM model across RMSE, MAE, and MAPE. These outcomes underscore the GRU model’s capacity as a robust tool for cryptocurrency value prediction. In summary, this study tackles the challenge of cryptocurrency price prediction while emphasizing the promising role of advanced neural network architectures, such as GRU, in enhancing prediction accuracy, thus offering valuable insights into financial forecasting. Penerbit UMP 2023-09 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/41610/1/document.pdf Mohd Haziq, Abdul Hadi and Nor Azuana, Ramli and Islam, Q. U. I. (2023) Predicting Bitcoin and Ethereum prices using long short-term memory and gated recurrent unit. Data Analytics and Applied Mathematics (DAAM), 4 (2). pp. 8-17. ISSN 2773-4854. (Published) https://doi.org/10.15282/daam.v4i2.10195 https://doi.org/10.15282/daam.v4i2.10195
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA Mathematics
QA75 Electronic computers. Computer science
spellingShingle QA Mathematics
QA75 Electronic computers. Computer science
Mohd Haziq, Abdul Hadi
Nor Azuana, Ramli
Islam, Q. U. I.
Predicting Bitcoin and Ethereum prices using long short-term memory and gated recurrent unit
description Predicting future prices of cryptocurrencies, including Bitcoin and Ethereum, presents a formidable challenge owing to their inherent volatility. This study applies Long Short-Term Memory (LSTM), a well-established recurrent neural network for time series forecasting, to predict Bitcoin and Ethereum values. Historical price data for both cryptocurrencies, sourced from Yahoo Finance, serves as the basis for analysis. The dataset undergoes an 80% training and 20% testing partition. Subsequently, LSTM models are developed and trained on both datasets. In parallel, the gated recurrent unit (GRU), recognized as an advanced variant of the LSTM model, is explored for comparative purposes. Performance evaluation utilizes fundamental metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results reveal an intriguing trend: both models exhibit superior performance when applied to the Ethereum dataset compared to the Bitcoin dataset. This observation suggests the potential presence of Ethereum-specific features or patterns that align more effectively with deep learning model architectures. Notably, the GRU model consistently outperforms the LSTM model across RMSE, MAE, and MAPE. These outcomes underscore the GRU model’s capacity as a robust tool for cryptocurrency value prediction. In summary, this study tackles the challenge of cryptocurrency price prediction while emphasizing the promising role of advanced neural network architectures, such as GRU, in enhancing prediction accuracy, thus offering valuable insights into financial forecasting.
format Article
author Mohd Haziq, Abdul Hadi
Nor Azuana, Ramli
Islam, Q. U. I.
author_facet Mohd Haziq, Abdul Hadi
Nor Azuana, Ramli
Islam, Q. U. I.
author_sort Mohd Haziq, Abdul Hadi
title Predicting Bitcoin and Ethereum prices using long short-term memory and gated recurrent unit
title_short Predicting Bitcoin and Ethereum prices using long short-term memory and gated recurrent unit
title_full Predicting Bitcoin and Ethereum prices using long short-term memory and gated recurrent unit
title_fullStr Predicting Bitcoin and Ethereum prices using long short-term memory and gated recurrent unit
title_full_unstemmed Predicting Bitcoin and Ethereum prices using long short-term memory and gated recurrent unit
title_sort predicting bitcoin and ethereum prices using long short-term memory and gated recurrent unit
publisher Penerbit UMP
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/41610/1/document.pdf
http://umpir.ump.edu.my/id/eprint/41610/
https://doi.org/10.15282/daam.v4i2.10195
https://doi.org/10.15282/daam.v4i2.10195
_version_ 1822924406000713728
score 13.235362

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