Regression based Analysis for Bitcoin Price Prediction


  • Azim Muhammad Fahmi
  • Noor Azah Samsudin
  • Aida Mustapha
  • Nazim Razali
  • Shamsul Kamal Ahmad Khalid





Bitcoin, Cryptocurrency, Price prediction, Data mining.


In 2017, a significant number of individuals profited from the staggering growth of the price of Bitcoin from $800 USD in January to almost $20,000 USD in December. Because the cryptocurrency market being relatively new when compared to traditional markets such as stocks, foreign exchange, and gold, there is a significant lack of studies in regard to predicting its price behavior. This research is interested in evaluating a number of regression-based algorithms in predicting the price of the Bitcoin (BTC) against United States Dollar (USD). Among the algorithms that will be investigated include the Linear Regression (LR), Neural Network Regression (NNR), Bayesian Linear Regression (BLR), and Boosted Decision Tree Regression (BDTR). By applying such regression-based analysis algorithms, the findings f should further help document the behavior of such a brand new, challenging yet extremely lucrative market.




[1] Nakamoto S (2008), Bitcoin: A peer-to-peer electronic cash system.

[2] McNally S (2018), Predicting the price of Bitcoin using Machine Learning. In: 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), 339-343. IEEE.

[3] Georgoula I, Pournarakis D, Bilanakos C, Sotiropoulos D, Giaglis GM (2015), Using time-series and sentiment analysis to detect the determinants of bitcoin prices.

[4] Shah D, Zhang K (2014), Bayesian regression and Bitcoin. In: 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton), 409-414. IEEE.

[5] Matta M, Lunesu I, Marchesi M (2015), Bitcoin spread prediction using social and web search media. In: Proceedings of DeCAT, 2015.

[6] L. Kristoufek (2015), What are the main drivers of the bitcoin price? Evidence from wavelet coherence analysis, PloS one, 10(4), p. e0123923.

[7] Vidal RD (2014), The fractal nature of bitcoin: Evidence from wavelet power spectra.

[8] Matta M, Lunesu I, Marchesi M (2015), The predictor impact of web search media on bitcoin trading volumes. In: 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K), vol. 1, 620-626.

[9] Greaves A, Benjamin A (2015), Using the bitcoin transaction graph to predict the price of bitcoin.

[10] Madan SS, Zhao A (2015), Automated bitcoin trading via machine learning algorithms.

[11] Hitam NA, Ismail AR (2018), Comparative Performance of Machine Learning Algorithms for Cryptocurrency Forecasting. Indonesian Journal of Electrical Engineering and Computer Science, 11(3).

[12] Guo T, Antulov-Fantulin N (2018), Predicting short-term bitcoin price fluctuations from buy and sell orders. arXiv preprint arXiv:1802.04065.

[13] 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, 6, 5427-5437.

[14] Kim YB, Jun GK, Wook K, Jae HI, Tae HK, Shin JK, Chang HK (2016), Predicting fluctuations in cryptocurrency transactions based on user comments and replies. PloS one, 11, no. 8, e0161197.

[15] Brown MS (2015), What IT Needs to Know About the Data Mining Process, available from 2015/07/29/what-it-needs-to-know-about-the-data-mining-process/ #4ade2420515f

[16] Wirth R, Hipp J (2000), CRISP-DM: Towards a standard process model for data mining.

[17] Yan, X., & Su, X. (2009). Linear regression analysis. Hackensack, N.J.: World Scientific.

[18] Specht, D. F. (1991). A general regression neural network. IEEE transactions on neural networks, 2(6), 568-576.

[19] Minka, T.P. (2009). Bayesian linear regression.

[20] Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77(4), 802-813.

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How to Cite

Muhammad Fahmi, A., Azah Samsudin, N., Mustapha, A., Razali, N., & Kamal Ahmad Khalid, S. (2018). Regression based Analysis for Bitcoin Price Prediction. International Journal of Engineering & Technology, 7(4.38), 1070–1073.
Received 2019-02-20
Accepted 2019-02-20
Published 2018-12-03