Bitcoin price prediction based on other cryptocurrencies using machine learning and time series analysis

Document Type : Article

Authors

1 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, P.O. Box 11155-4563, Iran

2 School of Industrial Engineering, Sharif University of Technology, Tehran, Iran

Abstract

Cryptocurrencies, which the Bitcoin is the most remarkable one, have allured substantial awareness up to now, and they have encountered enormous instability in their price. While some studies utilize conventional statistical and econometric ways to uncover the driving variables of Bitcoin's prices, experimentation on the advancement of predicting models to be used as decision support tools in investment techniques is rare. There are many different predicting cryptocurrencies' price methods that cover various purposes, such as forecasting a one-step approach that can be done through time series analysis, neural networks, and machine learning algorithms. Sometimes realizing the trend of a coin in a long run period is needed. In this paper, some machine learning algorithms are applied to find the best ones that can forecast Bitcoin price based on three other famous coins. Second, a new methodology is developed to predict Bitcoin's worth, this is also done by considering different cryptocurrencies prices (Ethereum, Zcash, and Litecoin). The results demonstrated that Zcash has the best performance in forecasting Bitcoin's price without any data on Bitcoin's fluctuations price among these three cryptocurrencies.

Keywords

References

References:
1. Bhattacharya, D., Mukhoti, J., and Konar, A. "Learning regularity in an economic time-series for structure prediction", Applied Soft Computing Journal, 76, pp. 31-44 (2019).
2. Corbet, S., Lucey, B., Urquhart, A., et al. "Cryptocurrencies as a financial asset: A systematic analysis", International Review of Financial Analysis, 62, pp. 182-199 (2019).
3. Yang, B., Sun, Y., and Wang, S. "A novel two-stage approach for cryptocurrency analysis", International Review of Financial Analysis, 72, 101567 (2020).
4. Fryzlewicz, P., Bellegem, S., and Sachs, R. "Forecasting non-stationary time series by wavelet process modelling", Annals of the Institute of Statistical Mathematics, 55, pp. 737-764 (2003).
5. Hossain, Z., Rahman, A., Hossain, et al. "Overdifferencing and forecasting with non-stationary time series data", Dhaka University Journal of Science, 67, pp. 21-26 (2019).
6. Arlinghaus, S.L., PHB Practical Handbook of Curve Fitting, In CRC Press (1994).
7. Dooley, G. and Lenihan, H. "An assessment of time series methods in metal price forecasting", Resources Policy, 30(3), pp. 208-217 (2005).
8. Ediger, V. and Akar, S. "ARIMA forecasting of primary energy demand by fuel in Turkey", Energy Policy, 35(3), pp. 1701-1708 (2007).
9. Khashei, M. and Bijari, M. "An artificial neural network (p, d, q) model for timeseries forecasting", Expert Systems with Applications, 37(1), pp. 479-489 (2010).
10. Khashei, M. and Bijari, M. "A novel hybridization of artificial neural networks and ARIMA models for time series forecasting", Applied Soft Computing, 11(2), pp. 2664-2675 (2011).
11. Martinez, F., Frias, M., Peerez-Godoy, M., et al. "Dealing with seasonality by narrowing the training set in time series forecasting with kNN", Expert Systems with Applications, 103, pp. 38-48 (2018).
12. Azadeh, A., Moghaddam, M., Khakzad, M. et al. "A flexible neural network-fuzzy mathematical programming algorithm for improvement of oil price estimation and forecasting", Computers & Industrial Engineering, 62(2), pp. 421-430 (2012).
13. Khandelwal, I., Adhikari, R., and Verma, G. "Time series forecasting using hybrid ARIMA and ANN models based on DWT decomposition", Procedia Computer Science, 48(1), pp. 173-179 (2015).
14. Buncic, D. and Moretto, C. "Forecasting copper prices with dynamic averaging and selection models", North American Journal of Economics and Finance, 33, pp. 1-38 (2015).
15. Laboissiere, L., Fernandes, R., and Lage, G. "Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks", Applied Soft Computing Journal, 35, pp. 66-74 (2015).
16. Fan, X., Wang, L., and Li, S. "Predicting chaotic coal prices using a multi-layer perceptron network model", Resources Policy, 50, pp. 86-92 (2016).
17. Gangopadhyay, K., Jangir, A., and Sensarma, R. "Forecasting the price of gold: An error correction approach", IIMB Management Review, 28(1), pp. 6-12 (2016).
18. Pradeepkumar, D. and Ravi, V. "Forecasting financial time series volatility using particle swarm optimization trained quantile regression neural network", Applied Soft Computing, 58, pp. 35-52 (2017).
19. Fischer, T. and Krauss, C. "Deep learning with long short-term memory networks for financial market predictions", European Journal of Operational Research, 270(2), pp. 654-669 (2018).
20. Sezer, O.B. and Ozbayoglu, A.M. "Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach", Applied Soft Computing, 70, pp. 525-538 (2018).
21. Demir, D., Gozgor, G., Lau, C.K.M., et al. "Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation", Finance Research Letters, 26, pp. 145-149 (2018).
22. Chou, J.S. and Tran, D.S. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders", Energy, 165, pp. 709-726 (2018).
23. Kim, H.Y. andWon, C.H. "Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models", Expert Systems with Applications, 103, pp. 25-37 (2018).
24. Phillips, P., Shi, S., and Yu, J. "Testing for multiple bubbles: Historical episodes of exuberance and collapse in the S&P 500", International Economic Review, 56(4), pp. 1043-1078 (2013).
25. Bouri, E., Shahzad, J., and Roubaud, D. "Co-Explosivity in the cryptocurrency market", Finance Research Letters, 29, pp. 178-183 (2018).
26. Bouri, E., Gupta, R., and Roubaud, D. "Herding behaviour in cryptocurrencies", Finance Research Letters, 29, pp. 216-221 (2018).
27. Ji, Q., Bouri, E., Lau, C.K., et al. "Dynamic connectedness and integration in cryptocurrency markets", International Review of Financial Analysis, 63, pp. 257-272 (2018).
28. Mallqui, D.C. and Fernandes, R.A. "Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques", Applied Soft Computing, 75, pp. 596-606 (2019).
29. Alameer, Z., Elaziz, M., Ewees, A., et al. "Forecasting gold price  fluctuations using improved multilayer perceptron neural network and whale optimization algorithm", Resources Policy, 61, pp. 250-260 (2019).
30. Cao, J., Li, Z., and Li, J. "Financial time series forecasting model based on CEEMDAN and LSTM", Physica A: Statistical Mechanics and its Applications, 519, pp. 127-139 (2019).
31. Atsalakis, G.S., Atsalaki, I.G., Pasiouras, F., et al. "Bitcoin price forecasting with neuro-fuzzy techniques", European Journal of Operational Research, 276, pp. 770-780 (2019).
32. Bouri, E., Roubaud, D., and Shahzad, J. "Do Bitcoin and other cryptocurrencies jump together?", The Quarterly Review of Economics and Finance, 76, pp. 396-409 (2019).
33. Bouri, E., Lucey, B., and Roubaud, D. "The volatility surprise of leading cryptocurrencies: Transitory and permanent linkages", Finance Research Letters, 33, 101188 (2020).
34. Qureshi, S., Aftab, M., Bouri, E., et al. "Dynamic interdependence of cryptocurrency markets: An analysis across time and frequency", Physica A: Statistical Mechanics and its Applications, 559, 125077 (2020).
Scientia Iranica
Volume 30, Issue 1
Transactions on Industrial Engineering (E)
January and February 2023
Pages 285-301

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