Comparison of different Artificial Neural Networks for Bitcoin price forecasting
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910541661863936 |
|---|---|
| author | Baumann, Silas Busch, Karl A. Gardi, Hamza A. A. |
| author_facet | Baumann, Silas Busch, Karl A. Gardi, Hamza A. A. |
| contents | This study investigates the impact of varying sequence lengths on the accuracy of predicting cryptocurrency returns using Artificial Neural Networks (ANNs). Utilizing the Mean Absolute Error (MAE) as a threshold criterion, we aim to enhance prediction accuracy by excluding returns that are smaller than this threshold, thus mitigating errors associated with minor returns. The subsequent evaluation focuses on the accuracy of predicted returns that exceed this threshold. We compare four sequence lengths 168 hours (7 days), 72 hours (3 days), 24 hours, and 12 hours each with a return prediction interval of 2 hours. Our findings reveal the influence of sequence length on prediction accuracy and underscore the potential for optimized sequence configurations in financial forecasting models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17930 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Comparison of different Artificial Neural Networks for Bitcoin price forecasting Baumann, Silas Busch, Karl A. Gardi, Hamza A. A. Machine Learning Artificial Intelligence This study investigates the impact of varying sequence lengths on the accuracy of predicting cryptocurrency returns using Artificial Neural Networks (ANNs). Utilizing the Mean Absolute Error (MAE) as a threshold criterion, we aim to enhance prediction accuracy by excluding returns that are smaller than this threshold, thus mitigating errors associated with minor returns. The subsequent evaluation focuses on the accuracy of predicted returns that exceed this threshold. We compare four sequence lengths 168 hours (7 days), 72 hours (3 days), 24 hours, and 12 hours each with a return prediction interval of 2 hours. Our findings reveal the influence of sequence length on prediction accuracy and underscore the potential for optimized sequence configurations in financial forecasting models. |
| title | Comparison of different Artificial Neural Networks for Bitcoin price forecasting |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2407.17930 |