The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information
Open accessAbstract
Bitcoin has recently attracted considerable attention in the fields of economics, cryptography, and computer science due to its inherent nature of combining encryption technology and monetary units. This paper reveals the effect of Bayesian neural networks (BNNs) by analyzing the time series of Bitcoin process. We also select the most relevant features from Blockchain information that is deeply involved in Bitcoin's supply and demand and use them to train models to improve the predictive performance of the latest Bitcoin pricing process. We conduct the empirical study that compares the Bayesian neural network with other linear and non-linear benchmark models on modeling and predicting the Bitcoin process. Our empirical studies show that BNN performs well in predicting Bitcoin price time series and explaining the high volatility of the recent Bitcoin price.
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Urquhart, A. (2016) · Economics Letters · doi:10.1016/j.econlet.2016.09.019 · Cited by 1,338
Cited by
RETRACTED: Cryptocurrencies as a financial asset: A systematic analysis
Corbet, S., Lucey, B., Urquhart, A., & Yarovaya, L. (2019) · International Review of Financial Analysis · doi:10.1016/j.irfa.2018.09.003 · Cited by 1,172
RetractedBlockchain and Machine Learning for Communications and Networking Systems
Liu, Y., Yu, F. R., Li, X., Ji, H., & Leung, V. C. M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2975911 · Cited by 313
Hamayel, M. J., & Owda, A. Y. (2021) · AI · doi:10.3390/ai2040030 · Cited by 294
An intelligent blockchain-based system for safe vaccine supply and supervision
Yong, B., Shen, J., Liu, X., Li, F., Chen, H., & Zhou, Q. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.10.009 · Cited by 258
Mallqui, D. C. A., & Fernandes, R. A. S. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2018.11.038 · Cited by 253
Metadata
- Authors
- Jang, H., & Lee, J.
- Venue
- IEEE Access
- Published
- 2018
- Volume
- 6
- Pages
- 5427-5437
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2775379762
- Cited by
- 457
- References
- 1
- FWCI
- 36.76
- Type
- journal article
- Language
- en
- Open access
- gold
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- Jang, H., & Lee, J. (2018). An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information. IEEE Access. https://doi.org/10.1109/access.2017.2779181
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
