Bitcoin Price Prediction Using Machine Learning and Artificial Neural Network Model

Alvin Ho; Ramesh Vatambeti; Sathish Kumar Ravichandran

doi:10.17485/IJST/v14i27.878

Article

Bitcoin Price Prediction Using Machine Learning and Artificial Neural Network Model

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v14i27.878

Year: 2021, Volume: 14, Issue: 27, Pages: 2300-2308

Original Article

Bitcoin Price Prediction Using Machine Learning and Artificial Neural Network Model

Alvin Ho¹, Ramesh Vatambeti^1*, Sathish Kumar Ravichandran¹

¹Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India

*Corresponding Author
Email: [email protected]

Received Date:20 May 2021, Accepted Date:15 July 2021, Published Date:11 August 2021

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: This paper explains the working of the linear regression and Long Short-Term Memory model in predicting the value of a Bitcoin. Due to its raising popularity, Bitcoin has become like an investment and works on the Block chain technology which also gave raise to other crypto currency. This makes it very difficult to predict its value and hence with the help of Machine Learning Algorithm and Artificial Neural Network Model this predictor is tested. Methodology: In this study, we have used data sets for Bitcoin for testing and training the ML and AI model. With the help of python libraries, the data filtration process was done. Python has provided with a best feature for data analysis and visualization. After the understanding of the data, we trim the data and use the features or attributes best suited for the model. Implementation of the model is done and the result is recorded. Finding: It was discovered that the linear regression model’s accuracy rate is very high when compared to other Machine Learning models from related works; it was found to be 99.87 percent accurate. The LSTM model, on the other hand, shows a mini error rate of 0.08 percent. This, in turn, demonstrates that the neural network model is more optimized than the machine learning model. Novelty: In this work, a small GUI has been created using the tkinter library that will allow the user to input the High, Low, and Open features values and then predict the next value for the coin. This paper compares the prediction outcomes of a machine learning model and an artificial neural network model. Because linear regression provided the highest accuracy compared to the other machine learning models, we used it to compare it to the LSTM model.

Keywords: Bitcoin; Block chain; Crypto currency; Machine Learning; Artificial Neural Network

References

Sin E, Wang L. Bitcoin price prediction using ensembles of neural networks. In: 2017 13th International conference on natural computation, fuzzy systems and knowledge discovery. (pp. 666-671) IEEE. 2017. 10.1109/FSKD.2017.8393351
Shankhdhar A, Singh AK, Naugraiya S, Saini PK. Bitcoin Price Alert and Prediction System using various Models. IOP Conference Series: Materials Science and Engineering. 2021;1131(1):012009. Available from: https://dx.doi.org/10.1088/1757-899x/1131/1/012009
Mittal R, Arora S, Bhatia MP. Automated cryptocurrencies prices prediction using machine learning. ICTACT Journal on Soft Computing. 2018;8(4):1758–1761. Available from: http://ictactjournals.in/paper/IJSC_Vol_8_Iss_4_Paper_8_1758_1761.pdf
Nakamoto S. Bitcoin: A peer-to-peer electronic cash system. 2019. Available from: https://git.dhimmel.com/bitcoin-whitepaper/
Sebastião H, Godinho P. Forecasting and trading cryptocurrencies with machine learning under changing market conditions. Financial Innovation. 2021;7(1):1–30. Available from: https://dx.doi.org/10.1186/s40854-020-00217-x
Phaladisailoed T, Numnonda T. Machine learning models comparison for bitcoin price prediction. 10th International Conference on Information Technology and Electrical Engineering. 2018;p. 506–511. Available from: 10.1109/ICITEED.2018.8534911
Jaquart P, Dann D, Weinhardt C. Short-term bitcoin market prediction via machine learning. The Journal of Finance and Data Science. 2021;7:45–66. Available from: 10.1016/j.jfds.2021.03.001
Rane PV, Dhage SN. Systematic erudition of bitcoin price prediction using machine learning techniques. 5th International Conference on Advanced Computing & Communication Systems (ICACCS). 2019;p. 594–598. Available from: 10.1109/ICACCS.2019.8728424
Roy R, Roy S, Hossain MN, Alam MZ, Nazmul N. Study on nonlinear partial differential equation by implementing MSE method. Global Scientific Journals. 2020;8(1):1651–1665.
Mckinney W. Pandas: a foundational Python library for data analysis and statistics. Python for High Performance and Scientific Computing. 2011;14(9):1–9. Available from: https://www.dlr.de/sc/portaldata/15/resources/dokumente/pyhpc2011/submissions/pyhpc2011_submission_9.pdf
Waskom M. Seaborn: statistical data visualization. Journal of Open Source Software. 2021;6(60):3021. Available from: https://dx.doi.org/10.21105/joss.03021
Abraham A, Pedregosa F, Eickenberg M, Gervais P, Mueller A, Kossaifi J, et al. Machine learning for neuroimaging with scikit-learn. Frontiers in Neuroinformatics. 2014;8:14. Available from: https://dx.doi.org/10.3389/fninf.2014.00014
Polo G. PyGTK, PyQT, Tkinter and wxPython comparison. . The Python Papers. 2008;3:26–37. Available from: https://www.mclibre.org/descargar/docs/revistas/the-python-papers/the-python-papers-3-1-en-200804.pdf
Awoke T, Rout M, Mohanty L, Satapathy SC. Bitcoin price prediction and analysis using deep learning models. In: Communication Software and Networks 2021. (pp. 631-640) Springer.. 10.1007/978-981-15-5397-4_63
Livieris IE, Stavroyiannis S, Pintelas E, Kotsilieris T, Pintelas P. A dropout weight-constrained recurrent neural network model for forecasting the price of major cryptocurrencies and CCi30 index. Evolving Systems. 2021;3:1–6. Available from: 10.1007/s12530-020-09361-2
An optimized support vector machine (SVM) based on particle swarm optimization (PSO) for cryptocurrency forecasting. Procedia Computer Science. 2019;163:427–433. Available from: https://doi.org/10.1016/j.procs.2019.12.125
Greaves A, Au B. Using the bitcoin transaction graph to predict the price of bitcoin. 2015. Available from: http://snap.stanford.edu/class/cs224w-2015/projects_2015/Using_the_Bitcoin_Transaction_Graph_to_Predict_the_Price_of_Bitcoin.pdf
Valencia F, Gómez-Espinosa A, Valdés-Aguirre B. Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning. Entropy. 2019;21(6):589. Available from: https://dx.doi.org/10.3390/e21060589
Chowdhury R, Rahman MA, Rahman MS, Mahdy MRC. An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning. Physica A: Statistical Mechanics and its Applications. 2020;551(124569). Available from: https://dx.doi.org/10.1016/j.physa.2020.124569
Akyildirim E, Goncu A, Sensoy A. Prediction of cryptocurrency returns using machine learning. Annals of Operations Research. 2021;297(1-2):3–36. Available from: https://dx.doi.org/10.1007/s10479-020-03575-y
Huang X, Zhang W, Huang Y, Tang X, Zhang M, Surbiryala J, et al. 2021.
Muniye TA, Mohanty MR, Satapathy S. Bitcoin Price Prediction and Analysis Using Deep Learning Models. In: Communication Software and Networks. 2020.

Copyright

© 2021 Ho et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)