Name
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%. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/PDP2018.2018.00060 | 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) |
Keywords | Field | DocType |
Bitcoin,Deep Learning,GPU,Recurrent Neural Network,Long Short Term Memory,ARIMA | Data mining,Time series,Central processing unit,Computer science,Recurrent neural network,Mean squared error,Price index,Autoregressive integrated moving average,Artificial intelligence,Deep learning,Machine learning,Bayesian probability | Conference |
ISSN | ISBN | Citations |
1066-6192 | 978-1-5386-4976-3 | 14 |
PageRank | References | Authors |
1.09 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sean McNally | 1 | 14 | 1.09 |
| Jason Roche | 2 | 14 | 1.09 |
| Simon Caton | 3 | 159 | 16.20 |