Development of a cryptocurrency price prediction model: leveraging GRU and LSTM for Bitcoin, Litecoin and Ethereum
Cryptocurrency represents a form of asset that has arisen from the progress of financial technology, presenting significant prospects for scholarly investigations. The ability to anticipate cryptocurrency prices with extreme accuracy is very desirable to researchers and investors. However, time-seri...
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| Format: | Article |
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PeerJ Inc.
2025-03-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2675.pdf |
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| author | Ramneet Kaur Mudita Uppal Deepali Gupta Sapna Juneja Syed Yasser Arafat Junaid Rashid Jungeun Kim Roobaea Alroobaea |
| author_facet | Ramneet Kaur Mudita Uppal Deepali Gupta Sapna Juneja Syed Yasser Arafat Junaid Rashid Jungeun Kim Roobaea Alroobaea |
| author_sort | Ramneet Kaur |
| collection | DOAJ |
| description | Cryptocurrency represents a form of asset that has arisen from the progress of financial technology, presenting significant prospects for scholarly investigations. The ability to anticipate cryptocurrency prices with extreme accuracy is very desirable to researchers and investors. However, time-series data presents significant challenges due to the nonlinear nature of the cryptocurrency market, complicating precise price predictions. Several studies have explored cryptocurrency price prediction using various deep learning (DL) algorithms. Three leading cryptocurrencies, determined by market capitalization, Ethereum (ETH), Bitcoin (BTC), and Litecoin (LTC), are examined for exchange rate predictions in this study. Two categories of recurrent neural networks (RNNs), specifically long short-term memory (LSTM) and gated recurrent unit (GRU), are employed. Four performance metrics are selected to evaluate the prediction accuracy namely mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) for three cryptocurrencies which demonstrates that GRU model outperforms LSTM. The GRU model was implemented as a two-layer deep learning network, optimized using the Adam optimizer with a dropout rate of 0.2 to prevent overfitting. The model was trained using normalized historical price data sourced from CryptoDataDownload, with an 80:20 train-test split. In this work, GRU qualifies as the best algorithm for developing a cryptocurrency price prediction model. MAPE values for BTC, LTC and ETH are 0.03540, 0.08703 and 0.04415, respectively, which indicate that GRU offers the most accurate forecasts as compared to LSTM. These prediction models are valuable for traders and investors, offering accurate cryptocurrency price predictions. Future studies should also consider additional variables, such as social media trends and trade volumes that may impact cryptocurrency pricing. |
| format | Article |
| id | doaj-art-8afd88645ff9449895368b69fdbbb9ff |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-8afd88645ff9449895368b69fdbbb9ff2025-08-20T03:41:46ZengPeerJ Inc.PeerJ Computer Science2376-59922025-03-0111e267510.7717/peerj-cs.2675Development of a cryptocurrency price prediction model: leveraging GRU and LSTM for Bitcoin, Litecoin and EthereumRamneet Kaur0Mudita Uppal1Deepali Gupta2Sapna Juneja3Syed Yasser Arafat4Junaid Rashid5Jungeun Kim6Roobaea Alroobaea7Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IndiaKIET Group of Institutions, Ghaziabad, IndiaMirpur University of Science and Technology, Mirpur, PakistanDepartment of Artificial Intelligence and Data Science, Sejong University, Seoul, Republic of South KoreaDepartment of Computer Science and Engineering, Inha University, Incheon, Republic of South KoreaDepartment of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaCryptocurrency represents a form of asset that has arisen from the progress of financial technology, presenting significant prospects for scholarly investigations. The ability to anticipate cryptocurrency prices with extreme accuracy is very desirable to researchers and investors. However, time-series data presents significant challenges due to the nonlinear nature of the cryptocurrency market, complicating precise price predictions. Several studies have explored cryptocurrency price prediction using various deep learning (DL) algorithms. Three leading cryptocurrencies, determined by market capitalization, Ethereum (ETH), Bitcoin (BTC), and Litecoin (LTC), are examined for exchange rate predictions in this study. Two categories of recurrent neural networks (RNNs), specifically long short-term memory (LSTM) and gated recurrent unit (GRU), are employed. Four performance metrics are selected to evaluate the prediction accuracy namely mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) for three cryptocurrencies which demonstrates that GRU model outperforms LSTM. The GRU model was implemented as a two-layer deep learning network, optimized using the Adam optimizer with a dropout rate of 0.2 to prevent overfitting. The model was trained using normalized historical price data sourced from CryptoDataDownload, with an 80:20 train-test split. In this work, GRU qualifies as the best algorithm for developing a cryptocurrency price prediction model. MAPE values for BTC, LTC and ETH are 0.03540, 0.08703 and 0.04415, respectively, which indicate that GRU offers the most accurate forecasts as compared to LSTM. These prediction models are valuable for traders and investors, offering accurate cryptocurrency price predictions. Future studies should also consider additional variables, such as social media trends and trade volumes that may impact cryptocurrency pricing.https://peerj.com/articles/cs-2675.pdfCryptocurrencyBitcoinLitecoinEthereumPrice predictionGated recurrent unit |
| spellingShingle | Ramneet Kaur Mudita Uppal Deepali Gupta Sapna Juneja Syed Yasser Arafat Junaid Rashid Jungeun Kim Roobaea Alroobaea Development of a cryptocurrency price prediction model: leveraging GRU and LSTM for Bitcoin, Litecoin and Ethereum PeerJ Computer Science Cryptocurrency Bitcoin Litecoin Ethereum Price prediction Gated recurrent unit |
| title | Development of a cryptocurrency price prediction model: leveraging GRU and LSTM for Bitcoin, Litecoin and Ethereum |
| title_full | Development of a cryptocurrency price prediction model: leveraging GRU and LSTM for Bitcoin, Litecoin and Ethereum |
| title_fullStr | Development of a cryptocurrency price prediction model: leveraging GRU and LSTM for Bitcoin, Litecoin and Ethereum |
| title_full_unstemmed | Development of a cryptocurrency price prediction model: leveraging GRU and LSTM for Bitcoin, Litecoin and Ethereum |
| title_short | Development of a cryptocurrency price prediction model: leveraging GRU and LSTM for Bitcoin, Litecoin and Ethereum |
| title_sort | development of a cryptocurrency price prediction model leveraging gru and lstm for bitcoin litecoin and ethereum |
| topic | Cryptocurrency Bitcoin Litecoin Ethereum Price prediction Gated recurrent unit |
| url | https://peerj.com/articles/cs-2675.pdf |
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