Closing Price Prediction of Cryptocurrencies BTC, LTC, and ETH Using a Hybrid ARIMA-LSTM Algorithm
This study aims to develop a hybrid algorithm using the ARIMA model and LSTM-type recurrent neural networks to predict the closing prices of the cryptocurrencies BTC, LTC, and ETH. The methodology includes an exploratory data analysis, followed by the design, implementation, and evaluation of each i...
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| Language: | English |
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2025-06-01
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| Series: | HighTech and Innovation Journal |
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| Online Access: | https://hightechjournal.org/index.php/HIJ/article/view/1170 |
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| author | Jherson S. Ruiz-Lopez Miguel Jiménez-Carrión |
| author_facet | Jherson S. Ruiz-Lopez Miguel Jiménez-Carrión |
| author_sort | Jherson S. Ruiz-Lopez |
| collection | DOAJ |
| description | This study aims to develop a hybrid algorithm using the ARIMA model and LSTM-type recurrent neural networks to predict the closing prices of the cryptocurrencies BTC, LTC, and ETH. The methodology includes an exploratory data analysis, followed by the design, implementation, and evaluation of each individual algorithm as well as the combined hybrid algorithm. The results, after experimentation and evaluation of metrics on the test set, indicated that the ARIMA model was inefficient in predicting the closing prices of cryptocurrencies. On the other hand, the hybrid model for BTC showed significant statistical differences in the metrics, with MAE = $726.21 and MAPE = 1.75%, compared to the LSTM model, which achieved MAE = $729.35 and MAPE = 1.76%. These results indicate better performance from the hybrid model. Regarding the RMSE metric, the hybrid model scored 1157.47, while LSTM scored 1159.99; although statistically equivalent, the hybrid model was numerically better. For the remaining metrics and other cryptocurrencies, both methods were statistically equivalent. For five-day-ahead predictions, the hybrid algorithm continued to yield better results for LTC and ETH.
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| format | Article |
| id | doaj-art-b0f98e431a93400bbbec3d0d4e22b67b |
| institution | DOAJ |
| issn | 2723-9535 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | HighTech and Innovation Journal |
| spelling | doaj-art-b0f98e431a93400bbbec3d0d4e22b67b2025-08-20T03:22:52ZengItal PublicationHighTech and Innovation Journal2723-95352025-06-016210.28991/HIJ-2025-06-02-09Closing Price Prediction of Cryptocurrencies BTC, LTC, and ETH Using a Hybrid ARIMA-LSTM AlgorithmJherson S. Ruiz-Lopez0https://orcid.org/0009-0007-6961-6136Miguel Jiménez-Carrión1https://orcid.org/0000-0001-9632-5085Faculty of Industrial Engineering, Universidad Nacional de Piura, Castilla-Piura, 20002Faculty of Industrial Engineering, Universidad Nacional de Piura, Castilla-Piura, 20002This study aims to develop a hybrid algorithm using the ARIMA model and LSTM-type recurrent neural networks to predict the closing prices of the cryptocurrencies BTC, LTC, and ETH. The methodology includes an exploratory data analysis, followed by the design, implementation, and evaluation of each individual algorithm as well as the combined hybrid algorithm. The results, after experimentation and evaluation of metrics on the test set, indicated that the ARIMA model was inefficient in predicting the closing prices of cryptocurrencies. On the other hand, the hybrid model for BTC showed significant statistical differences in the metrics, with MAE = $726.21 and MAPE = 1.75%, compared to the LSTM model, which achieved MAE = $729.35 and MAPE = 1.76%. These results indicate better performance from the hybrid model. Regarding the RMSE metric, the hybrid model scored 1157.47, while LSTM scored 1159.99; although statistically equivalent, the hybrid model was numerically better. For the remaining metrics and other cryptocurrencies, both methods were statistically equivalent. For five-day-ahead predictions, the hybrid algorithm continued to yield better results for LTC and ETH. https://hightechjournal.org/index.php/HIJ/article/view/1170LSTM NetworksARIMA ModelHybrid ApproachPredictionCryptocurrencies |
| spellingShingle | Jherson S. Ruiz-Lopez Miguel Jiménez-Carrión Closing Price Prediction of Cryptocurrencies BTC, LTC, and ETH Using a Hybrid ARIMA-LSTM Algorithm HighTech and Innovation Journal LSTM Networks ARIMA Model Hybrid Approach Prediction Cryptocurrencies |
| title | Closing Price Prediction of Cryptocurrencies BTC, LTC, and ETH Using a Hybrid ARIMA-LSTM Algorithm |
| title_full | Closing Price Prediction of Cryptocurrencies BTC, LTC, and ETH Using a Hybrid ARIMA-LSTM Algorithm |
| title_fullStr | Closing Price Prediction of Cryptocurrencies BTC, LTC, and ETH Using a Hybrid ARIMA-LSTM Algorithm |
| title_full_unstemmed | Closing Price Prediction of Cryptocurrencies BTC, LTC, and ETH Using a Hybrid ARIMA-LSTM Algorithm |
| title_short | Closing Price Prediction of Cryptocurrencies BTC, LTC, and ETH Using a Hybrid ARIMA-LSTM Algorithm |
| title_sort | closing price prediction of cryptocurrencies btc ltc and eth using a hybrid arima lstm algorithm |
| topic | LSTM Networks ARIMA Model Hybrid Approach Prediction Cryptocurrencies |
| url | https://hightechjournal.org/index.php/HIJ/article/view/1170 |
| work_keys_str_mv | AT jhersonsruizlopez closingpricepredictionofcryptocurrenciesbtcltcandethusingahybridarimalstmalgorithm AT migueljimenezcarrion closingpricepredictionofcryptocurrenciesbtcltcandethusingahybridarimalstmalgorithm |