Revolutionizing Bitcoin price forecasts: A comparative study of advanced hybrid deep learning architectures
•Hybrid deep learning model outperforms basic LSTM in Bitcoin forecasting.•NRBO-CNN-BiLSTM-Attention model cuts MAPE by over 50 %.•Significant accuracy in 5-day and 15-day Bitcoin price forecasts.•Model tested across multiple loss functions for robustness.•Suggests dataset restructuring to improve m...
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| Vydané v: | Finance research letters Ročník 69; s. 106136 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Inc
01.11.2024
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| ISSN: | 1544-6123 |
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| Abstract | •Hybrid deep learning model outperforms basic LSTM in Bitcoin forecasting.•NRBO-CNN-BiLSTM-Attention model cuts MAPE by over 50 %.•Significant accuracy in 5-day and 15-day Bitcoin price forecasts.•Model tested across multiple loss functions for robustness.•Suggests dataset restructuring to improve model performance.
This paper employs a deep learning network with a comprehensive architecture to forecast Bitcoin prices, enhancing accuracy by integrating two meta-heuristic optimization algorithms, INFO and NRBO. Empirical results demonstrate that the hybrid model significantly outperforms the LSTM in both fit and predictive accuracy across in-sample and out-of-sample data. Notably, the NRBO-CNN-BiLSTM-Attention model substantially improves accuracy in 5-day and 15-day forecasts, reducing the MAPE by over 50 % compared to the LSTM model, thereby significantly enhancing overall predictive performance. The robustness of our results is supported by the MCS tests. Furthermore, strategically modifying time steps in data analysis optimizes model performance. |
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| AbstractList | •Hybrid deep learning model outperforms basic LSTM in Bitcoin forecasting.•NRBO-CNN-BiLSTM-Attention model cuts MAPE by over 50 %.•Significant accuracy in 5-day and 15-day Bitcoin price forecasts.•Model tested across multiple loss functions for robustness.•Suggests dataset restructuring to improve model performance.
This paper employs a deep learning network with a comprehensive architecture to forecast Bitcoin prices, enhancing accuracy by integrating two meta-heuristic optimization algorithms, INFO and NRBO. Empirical results demonstrate that the hybrid model significantly outperforms the LSTM in both fit and predictive accuracy across in-sample and out-of-sample data. Notably, the NRBO-CNN-BiLSTM-Attention model substantially improves accuracy in 5-day and 15-day forecasts, reducing the MAPE by over 50 % compared to the LSTM model, thereby significantly enhancing overall predictive performance. The robustness of our results is supported by the MCS tests. Furthermore, strategically modifying time steps in data analysis optimizes model performance. |
| ArticleNumber | 106136 |
| Author | He, Xiangyi Li, Houjian Li, Yiwei |
| Author_xml | – sequence: 1 givenname: Xiangyi orcidid: 0009-0009-1658-9845 surname: He fullname: He, Xiangyi email: 202103260@stu.sicau.edu.cn organization: College of Economics, Sichuan Agricultural University, Wenjiang District, Chengdu 611130, China – sequence: 2 givenname: Yiwei orcidid: 0000-0003-2386-3055 surname: Li fullname: Li, Yiwei email: yiwei.li@essex.ac.uk organization: Essex Business School, University of Essex, United Kingdom – sequence: 3 givenname: Houjian orcidid: 0000-0003-4852-8042 surname: Li fullname: Li, Houjian email: 14159@sicau.edu.cn organization: College of Economics, Sichuan Agricultural University, Wenjiang District, Chengdu 611130, China |
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| Cites_doi | 10.1016/j.frl.2019.101386 10.1016/j.resourpol.2020.101666 10.1016/j.engappai.2023.107532 10.1016/j.infoecopol.2017.02.002 10.1016/j.econlet.2018.02.001 10.1016/j.frl.2022.103391 10.1016/j.najef.2021.101379 10.1016/j.techfore.2023.122938 10.1016/j.frl.2017.11.009 10.1007/s12197-020-09526-4 10.1007/s12599-017-0506-0 10.1016/j.energy.2018.03.099 10.1186/s40854-020-00176-3 10.1016/j.eswa.2021.114747 10.1016/j.eswa.2022.116516 10.1080/13504851.2014.916379 10.1016/j.dss.2016.12.001 10.1016/j.inffus.2023.101819 10.1016/j.asoc.2018.11.038 10.1016/j.econmod.2020.05.003 10.1016/j.energy.2011.05.004 10.1016/j.iref.2023.04.013 10.1016/j.geoderma.2017.06.020 10.1016/j.eswa.2022.116804 10.1109/ACCESS.2018.2841987 10.1016/j.frl.2022.103143 10.1016/j.engappai.2024.107991 10.1111/jofi.13119 10.1016/j.frl.2018.09.014 |
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| Snippet | •Hybrid deep learning model outperforms basic LSTM in Bitcoin forecasting.•NRBO-CNN-BiLSTM-Attention model cuts MAPE by over 50 %.•Significant accuracy in... |
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| SubjectTerms | Bitcoin price Hybrid models Meta-heuristic optimization algorithms Price forecast |
| Title | Revolutionizing Bitcoin price forecasts: A comparative study of advanced hybrid deep learning architectures |
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