The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Predicting the Price of Bitcoin Using Machine Learning
Abstract
The goal of this paper is to ascertain with what accuracy the direction of Bitcoin price in USD can be predicted. The price data is sourced from the Bitcoin Price Index. The task is achieved with varying degrees of success through the implementation of a Bayesian optimised recurrent neural network (RNN) and a Long Short Term Memory (LSTM) network. The LSTM achieves the highest classification accuracy of 52% and a RMSE of 8%. The popular ARIMA model for time series forecasting is implemented as a comparison to the deep learning models. As expected, the non-linear deep learning methods outperform the ARIMA forecast which performs poorly. Finally, both deep learning models are benchmarked on both a GPU and a CPU with the training time on the GPU outperforming the CPU implementation by 67.7%.
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Cites
Practical Bayesian Optimization of Machine Learning Algorithms
Snoek, J., Larochelle, H., & Adams, R. P. (2012) · arXiv · doi:10.48550/arxiv.1206.2944 · Cited by 6,691
The KDD process for extracting useful knowledge from volumes of data
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996) · Communications of the ACM · doi:10.1145/240455.240464 · Cited by 1,934
Cited by
A Comparison between ARIMA, LSTM, and GRU for Time Series Forecasting
Yamak, P. T., Yujian, L., & Gadosey, P. K. (2019) · Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence · doi:10.1145/3377713.3377722 · Cited by 380
Short-term stock market price trend prediction using a comprehensive deep learning system
Shen, J., & Shafiq, M. O. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00333-6 · Cited by 380
Mallqui, D. C. A., & Fernandes, R. A. S. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2018.11.038 · Cited by 253
Machine Learning Algorithm for Cryptocurrencies Price Prediction
Awotunde, J. B., Ogundokun, R. O., Jimoh, R. G., Misra, S., & Aro, T. O. (2021) · Studies in Computational Intelligence · doi:10.1007/978-3-030-72236-4_17 · Cited by 58
Hasanli, R., & Dursun, M. (2025) · FinTech · doi:10.3390/fintech4040077 · Cited by 3
“These approaches can learn hidden dependencies and evolving temporal patterns from large volumes of historical data, outperforming classical statistical methods in cryptocurrency price predictions [12,13].”
Metadata
- Authors
- McNally, S., Roche, J., & Caton, S.
- Venue
- 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)
- Publisher
- IEEE
- Published
- 2018-03
- Pages
- 339-343
- ISSN
- 1066-6192 (ISSN-L)
- OpenAlex
- W2586112617
- Cited by
- 665
- References
- 2
- FWCI
- 99.98
- Type
- conference paper
- Language
- en
- Open access
- closed
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- McNally, S., Roche, J., & Caton, S. (2018). Predicting the Price of Bitcoin Using Machine Learning. 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP). https://doi.org/10.1109/pdp2018.2018.00060
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