Implementation of LSTM-RNN for Bitcoin Prediction
DOI:
https://doi.org/10.34818/INDOJC.2021.6.3.592Keywords:
Bitcoin, Prediction, LSTM-RNN, RMSEAbstract
Bitcoin is a cryptocurrency that is used worldwide for digital payments or simply for investment purposes. Bitcoin is a new technology so there are currently very few prices prediction models available. Problems arise when someone uses bitcoin without understanding strong fundamentals. This can result in a lot of loss for the person. These problems certainly need to be overcome by predicting bitcoin prices using a machine learning approach. The purpose of this research is to predict the bitcoin USD price using the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model. The LSTM-RNN model was chosen because it is better than the traditional neural network model. Measurement of the results in this study using the Root Mean Square Error (RMSE). The RMSE results obtained on the application of the LSTM-RNN model 6461.14.
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References
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[17] https://finance.yahoo.com/quote/BTC-USD?p=BTC-USD. Accessed, 27 October 2021.
[2] Jang, Huisu, and Jaewook Lee. "An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information." Ieee Access 6 (2017): 5427-5437.
[3] de Oliveira, F. Andrade, and L. Enrique ZÃ. "¡ rate and M. de Azevedo Reis; C." Neri Nobre,“The use of artificial neural networks in the analysis and prediction of stock prices,†in IEEE International Conference on Systems, Man, and Cybernetics. 2011.
[4] Nemes, Magdalena Daniela, and Alexandru Butoi. "Data Mining on Romanian Stock Market Using Neural Networks for Price Prediction." informatica Economica 17.3 (2013).
[5] Sin, Edwin, and Lipo Wang. "Bitcoin price prediction using ensembles of neural networks." 2017 13th International conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD). IEEE, 2017.
[6] Ji, Suhwan, Jongmin Kim, and Hyeonseung Im. "A comparative study of bitcoin price prediction using deep learning." Mathematics 7.10 (2019): 898.
[7] Phaladisailoed, Thearasak, and Thanisa Numnonda. "Machine learning models comparison for bitcoin price prediction." 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE). IEEE, 2018.
[8] Amjad, Muhammad, and Devavrat Shah. "Trading bitcoin and online time series prediction." NIPS 2016 Time Series Workshop. PMLR, 2017.
[9] Chen, Zheshi, Chunhong Li, and Wenjun Sun. "Bitcoin price prediction using machine learning: An approach to sample dimension engineering." Journal of Computational and Applied Mathematics 365 (2020): 112395.
[10] Karasu, Seçkin, et al. "Prediction of Bitcoin prices with machine learning methods using time series data." 2018 26th signal processing and communications applications conference (SIU). IEEE, 2018.
[11] Azari, Amin. "Bitcoin price prediction: An ARIMA approach." arXiv preprint arXiv:1904.05315. 2019.
[12] Shu, Min, and Wei Zhu. "Real-time prediction of Bitcoin bubble crashes." Physica A: Statistical Mechanics and its Applications 548 (2020): 124477.
[13] McNally, Sean, Jason Roche, and Simon Caton. "Predicting the price of bitcoin using machine learning." 2018 26th euromicro international conference on parallel, distributed and network-based processing (PDP). IEEE, 2018.
[14] Sak, Hasim, Andrew W. Senior, and Françoise Beaufays. "Long short-term memory recurrent neural network architectures for large scale acoustic modeling.". 2014.
[15] F. A. Gers, N. N. Schraudolph, and J. Schmidhuber, “Learning precise timing with LSTM recurrent networks,†Journal of Ma-chine Learning Research, vol. 3, pp. 115–143, Mar. 2003.
[16] Kumar, Jitendra, Rimsha Goomer, and Ashutosh Kumar Singh. "Long short term memory recurrent neural network (LSTM-RNN) based workload forecasting model for cloud datacenters." Procedia Computer Science 125. 676-682. 2018.
[17] https://finance.yahoo.com/quote/BTC-USD?p=BTC-USD. Accessed, 27 October 2021.
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Published
2021-12-31
How to Cite
Nur Ghaniaviyanto Ramadhan, Nia Annisa Ferani Tanjung, & Faisal Dharma Adhinata. (2021). Implementation of LSTM-RNN for Bitcoin Prediction. Indonesian Journal on Computing (Indo-JC), 6(3), 17–24. https://doi.org/10.34818/INDOJC.2021.6.3.592
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Software Engineering
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