Predicting Bitcoin Market Price: A Comparative Analysis of ARIMA and Linear Regression Models
Main Article Content
Abstract
In recent years, Bitcoin has garnered significant attention from a diverse range of individuals, including academic researchers and trade investors. Bitcoin is widely recognized as the first and preeminent cryptocurrency in existence. Since its inception in 2009, the trading system of Bitcoin has gained significant popularity across diverse demographics due to its decentralized nature and the notable price fluctuations associated with the cryptocurrency. This article introduces a viable model for accurately forecasting the market price of Bitcoin through the utilization of several statistical analysis. The study was conducted using a dataset spanning five and a half years of bitcoin data, specifically from 2015 to 2020. The analysis employed a time series analysis technique known as the autoregressive integrated moving average (ARIMA) model. Moreover, it is also subjected to comparison with an established machine learning technique known as the linear regression (LR) model. The extensive prediction results demonstrate that the ARIMA model, when compared to the LR model, exhibits greater performance in determining short-term volatility in the weighted costs of bitcoin.
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
References
S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” 2008.
M. Briere, K. Oosterlinck, and A. Szafarz, “Virtual currency, tangible ` return: Portfolio diversification with bitcoins,” Tangible Return: Portfolio Diversification with Bitcoins (September 12, 2013), 2013.
I. Kaastra and M. Boyd, “Designing a neural network for forecasting financial and economic time series,” Neurocomputing, vol. 10, no. 3, pp. 215–236, 1996.
H. White, “Economic prediction using neural networks: The case of ibm daily stock returns,” in Neural Networks, 1988., IEEE International Conference on. IEEE, 1988, pp. 451–458.
C. Chatfield and M. Yar, “Holt-winters forecasting: some practical issues,” The Statistician, pp. 129–140, 1988.
B. Scott, “Bitcoin academic paper database,” suitpossum blog, 2016.
M. D. Rechenthin, “Machine-learning classification techniques for the analysis and prediction of high-frequency stock direction,” 2014.
D. Shah and K. Zhang, “Bayesian regression and bitcoin,” in Communication, Control, and Computing (Allerton), 2014 52nd Annual Allerton Conference on. IEEE, 2014, pp. 409–414.
G. H. Chen, S. Nikolov, and D. Shah, “A latent source model for nonparametric time series classification,” in Advances in Neural Information Processing Systems, 2013, pp. 1088–1096. [10] I. Georgoula, D. Pournarakis, C. Bilanakos, D. N. Sotiropoulos, and G. M. Giaglis, “Using time-series and sentiment analysis to detect the determinants of bitcoin prices,” Available at SSRN 2607167, 2015.
M. Matta, I. Lunesu, and M. Marchesi, “Bitcoin spread prediction using social and web search media,” Proceedings of DeCAT, 2015.
——, “The predictor impact of web search media on bitcoin trading volumes.”
B. Gu, P. Konana, A. Liu, B. Rajagopalan, and J. Ghosh, “Identifying information in stock message boards and its implications for stock market efficiency,” in Workshop on Information Systems and Economics, Los Angeles, CA, 2006.
A. Greaves and B. Au, “Using the bitcoin transaction graph to predict the price of bitcoin,” 2015.
I. Madan, S. Saluja, and A. Zhao, “Automated bitcoin trading via machine learning algorithms,” 2015.
R. Delfin Vidal, “The fractal nature of bitcoin: Evidence from wavelet power spectra,” The Fractal Nature of Bitcoin: Evidence from Wavelet Power Spectra (December 4, 2014), 2014.
L. Kristoufek, “What are the main drivers of the bitcoin price? evidence from wavelet coherence analysis,” PloS one, vol. 10, no. 4, p. e0123923, 2015.
Y. Yoon and G. Swales, “Predicting stock price performance: A neural network approach,” in System Sciences, 1991. Proceedings of the Twenty-Fourth Annual Hawaii International Conference on, vol. 4. IEEE, 1991, pp. 156–162.
T. Koskela, M. Lehtokangas, J. Saarinen, and K. Kaski, “Time series prediction with multilayer perceptron, fir and elman neural networks,” in Proceedings of the World Congress on Neural Networks. Citeseer, 1996, pp. 491–496.
C. L. Giles, S. Lawrence, and A. C. Tsoi, “Noisy time series prediction using recurrent neural networks and grammatical inference,” Machine learning, vol. 44, no. 1-2, pp. 161–183, 2001.