| Autor: |
Muhammad Iqbal, Arshad Iqbal, Abdullah Alshammari, Ihtisham Ali, Louai A. Maghrabi, Nighat Usman |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 12, Pp 118169-118184 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3448234 |
| Popis: |
Decentralized cryptocurrencies like Bitcoin are digital assets with a price volatility nature, that allow for blockchain-based, peer-to-peer monetary transactions. Due to the price volatility problem with decentralized cryptocurrencies, research into the underlying pricing mechanism is required. Additionally, the behavior of Bitcoin prices is non-stationary, meaning that the statistical distribution of data varies over time. The proposed framework demonstrates the use of sophisticated machine learning models in predicting the short and medium-term trends and actual values of Bitcoin prices. This research goes beyond previous work that has only looked at machine learning-based categorization for a single day by instead using such models to forecast price changes seven, thirty, and ninety days into the future. The generated models are useful and work admirably, with the classification models reaching a maximum accuracy enhancement up to 31.48% for a 90-day prediction and a 11.76% F1-score for a forecast extending to the thirtieth day. In the case of regression the margin of error shifts from the one-time horizon for price projections to the next. A significant notable downfall occurs in different error metrics. These findings suggest that the models given here outperform those already found in the literature. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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