Comparative Analysis of LSTM and GRU Models for Ethereum (ETH) Price Prediction
The increasing use of cryptocurrencies has changed the dynamics of investment, presenting both opportunities and challenges for investors. Although various studies have compared the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) in predicting financial asset prices, ther...
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Research Collaboration Community (RCC)
2025-02-01
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| Series: | International Journal of Business, Economics, and Social Development |
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| Online Access: | https://journal.rescollacomm.com/index.php/ijbesd/article/view/887 |
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| author | Moch Panji Agung Saputra Riza Andrian Ibrahim Renda Sandi Saputra |
| author_facet | Moch Panji Agung Saputra Riza Andrian Ibrahim Renda Sandi Saputra |
| author_sort | Moch Panji Agung Saputra |
| collection | DOAJ |
| description | The increasing use of cryptocurrencies has changed the dynamics of investment, presenting both opportunities and challenges for investors. Although various studies have compared the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) in predicting financial asset prices, there are still differences in results regarding which model is superior. Therefore, this study aims to compare the performance of LSTM and GRU in predicting Ethereum prices using a hyperparameter tuning approach. The data used is historical data of Ethereum (ETH) shares from 2020 to 2025. The research methodology includes data preprocessing using Min-Max scaling, model development with various layer configurations, and comprehensive evaluation using several performance metrics. The results show that the GRU Model provides superior performance with a lower Root Mean Squared Error (RMSE) of 0.0234 and Mean Absolute Error (MAE) of 0.0168, compared to LSTM's RMSE of 0.0265 and MAE of 0.0193. While LSTM exhibits a slightly better Mean Absolute Percentage Error (MAPE) of 18.08% compared to GRU at 18.17%, the GRU model achieves a higher R² Score of 0.9442 compared to LSTM at 0.9282. Visual analysis of the prediction patterns and residual distributions further demonstrates GRU’s more consistent and accurate performance in capturing Ethereum price movements. These findings suggest that while both models are effective for cryptocurrency price prediction, GRU offers slightly better overall performance and stability, especially in maintaining consistent prediction accuracy across different market conditions. |
| format | Article |
| id | doaj-art-66a09c7bf4b84a8a8ea3feb7adcd8f07 |
| institution | OA Journals |
| issn | 2722-1164 2722-1156 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Research Collaboration Community (RCC) |
| record_format | Article |
| series | International Journal of Business, Economics, and Social Development |
| spelling | doaj-art-66a09c7bf4b84a8a8ea3feb7adcd8f072025-08-20T02:10:32ZengResearch Collaboration Community (RCC)International Journal of Business, Economics, and Social Development2722-11642722-11562025-02-016113213810.46336/ijbesd.v6i1.887634Comparative Analysis of LSTM and GRU Models for Ethereum (ETH) Price PredictionMoch Panji Agung Saputra0Riza Andrian Ibrahim1Renda Sandi Saputra2Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang, IndonesiaDoctoral Mathematics Study Programme, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang, IndonesiaDepartment of Informatics, Faculty of Technology and Information, University of Informatics and Business, Bandung, IndonesiaThe increasing use of cryptocurrencies has changed the dynamics of investment, presenting both opportunities and challenges for investors. Although various studies have compared the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) in predicting financial asset prices, there are still differences in results regarding which model is superior. Therefore, this study aims to compare the performance of LSTM and GRU in predicting Ethereum prices using a hyperparameter tuning approach. The data used is historical data of Ethereum (ETH) shares from 2020 to 2025. The research methodology includes data preprocessing using Min-Max scaling, model development with various layer configurations, and comprehensive evaluation using several performance metrics. The results show that the GRU Model provides superior performance with a lower Root Mean Squared Error (RMSE) of 0.0234 and Mean Absolute Error (MAE) of 0.0168, compared to LSTM's RMSE of 0.0265 and MAE of 0.0193. While LSTM exhibits a slightly better Mean Absolute Percentage Error (MAPE) of 18.08% compared to GRU at 18.17%, the GRU model achieves a higher R² Score of 0.9442 compared to LSTM at 0.9282. Visual analysis of the prediction patterns and residual distributions further demonstrates GRU’s more consistent and accurate performance in capturing Ethereum price movements. These findings suggest that while both models are effective for cryptocurrency price prediction, GRU offers slightly better overall performance and stability, especially in maintaining consistent prediction accuracy across different market conditions.https://journal.rescollacomm.com/index.php/ijbesd/article/view/887ethereum price prediction, deep learning, long short-term memory (lstm), gated recurrent unit (gru), cryptocurrency, hyperparameter tuning |
| spellingShingle | Moch Panji Agung Saputra Riza Andrian Ibrahim Renda Sandi Saputra Comparative Analysis of LSTM and GRU Models for Ethereum (ETH) Price Prediction International Journal of Business, Economics, and Social Development ethereum price prediction, deep learning, long short-term memory (lstm), gated recurrent unit (gru), cryptocurrency, hyperparameter tuning |
| title | Comparative Analysis of LSTM and GRU Models for Ethereum (ETH) Price Prediction |
| title_full | Comparative Analysis of LSTM and GRU Models for Ethereum (ETH) Price Prediction |
| title_fullStr | Comparative Analysis of LSTM and GRU Models for Ethereum (ETH) Price Prediction |
| title_full_unstemmed | Comparative Analysis of LSTM and GRU Models for Ethereum (ETH) Price Prediction |
| title_short | Comparative Analysis of LSTM and GRU Models for Ethereum (ETH) Price Prediction |
| title_sort | comparative analysis of lstm and gru models for ethereum eth price prediction |
| topic | ethereum price prediction, deep learning, long short-term memory (lstm), gated recurrent unit (gru), cryptocurrency, hyperparameter tuning |
| url | https://journal.rescollacomm.com/index.php/ijbesd/article/view/887 |
| work_keys_str_mv | AT mochpanjiagungsaputra comparativeanalysisoflstmandgrumodelsforethereumethpriceprediction AT rizaandrianibrahim comparativeanalysisoflstmandgrumodelsforethereumethpriceprediction AT rendasandisaputra comparativeanalysisoflstmandgrumodelsforethereumethpriceprediction |