Saad Ali. Alahmari,



Support Vector Regression,Cryptocurrency,Machine Learning,Time-series Analysis. Non-linear,


The rising profit potential in virtual currency has made forecasting the prices of crypto currency a fascinating subject of study. Numerous studies have already been conducted to predict future prices of a specific virtual currency using a machine-learning model. However, very few have focused on using different kernels of a “Support Vector Regression” (SVR) model. This study applies the Linear, Polynomial and “Radial Basis Function”(RBF) kernels to predict the prices of the three major crypto currencies, Bitcoin, XRP and Ethereum, using a bivariate time series method employing the cryptocurrency (daily-Closed Price) as the continuous dependent variable and the “Morgan Stanley Capital International” (MSCI) World Index (MSCI-WI) and the (daily-Closed Price) as the predictor variable. The results demonstrated that ‘RBF’ outperforms most other kernel methods in predicting cryptocurrency prices in terms of “Mean Absolute Error”(MAE), “Mean Squared Error” (MSE), “Root Mean Squared Error” (RMSE) and R-squared (


I. “Coinmarktcap,” http://www. (accessed 18 Dec. 2018).

II. “Investing,” (accessed 15 June 2018).

III. “Kaggle,” (accessed 15 June 2018).

IV. B. Alex Greaves, “Using the Bitcoin transaction graph to predict the price of Bitcoin.”

V. C. Giakloglou and P. Newbold, “Empirical evidence on Dickey‐Fuller‐type tests,” Journal of Time Series Analysis, vol. 13, pp. 471–483, 1992, doi:10.1111/j.1467-9892.1992.tb00121.x.

VI. Das, Debojyoti, and KannadhasanManoharan. “Emerging stock market co-movements in South Asia: wavelet approach.” International Journal of Managerial Finance 15, no. 2 (2019): 236-256.

VII. . H. Drucker, C. Burges, L. Kaufman, A. Smola, and V. Vapnik, “Support vector regression machines,” in M. Mozer, M. Jordan, and T. Petsche, Eds., Advances in Neural Information Processing Systems 9, Cambridge, MA, USA: MIT Press, 1997, pp. 155–161.

VIII. H. Drucker, C. Burges, L. Kaufman, A. Smola, and V. Vapnik, “Support vector regression machines,” in M. Mozer, M. Jordan, and T. Petsche, Eds., Advances in Neural Information Processing Systems 9, Cambridge, MA, USA: MIT Press, 1997, pp. 155–161.

IX. H. Sun and B. Yu, “Forecasting financial returns volatility: A GARCH-SVR model,” Computational Economics, pp. 1–21, 2019.

X. H. Wang and D. Xu, “Parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function,”Journal of Control Science and Engineering, 2017.

XI. J. Huisu and J. Lee. “An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information.” IEEE Access, 6 ,pp. 5427-5437.2017.

XII. J. Rebane, I. Karlsson and P. Papapetrou, “Seq2Seq RNNs and ARIMA models for cryptocurrency prediction: A comparative study,” in Proceedings of SIGKDD Workshop on Fintech (SIGKDD Fintech’18), London, UK, Association for Computing Machinery (ACM), 2018, article id 4.

XIII. K. Müller, A. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen, and V. Vapnik, “Predicting time series with support vector machines,” in International Conference on Artificial Neural Networks, Berlin,Heidelberg: Springer, pp. 999–1004, 1997.

XIV. L. Catania, S. Grassi, and F. Ravazzolo, “Forecasting cryptocurrencies under model and parameter instability,” International Journal of Forecasting, vol. 35, no. 2, pp. 485–501, 2019.

XV. M. Razi and K. Athappilly, “A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models,” Expert Systems with Applications, vol. 29, no. 1, pp. 65–74, 2005.

XVI. M. Suganyadevi and C. K. Babulal, “Support vector regression model for the prediction of loadability margin of a power system,” Applied Soft Computing, vol. 24, pp. 304–315, 2014.

XVII. S. Alahmari, “Using machine learning ARIMA to predict the price of cryptocurrencies,” The ISC International Journal of Information Security, vol. 11, no. 3, pp. 139–144, 2019, doi: 10.22042/isecure.2019.11.0.18.

XVIII. S. McNally, J. Roche and S. Caton, “Predicting the price of Bitcoin using machine learning,” in 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), Cambridge, 2018, pp. 339–343, doi: 10.1109/PDP2018.2018.00060.

XIX. S. Wang, R. Li, and M. Guo, “Application of nonparametric regression in predicting traffic incident duration,” Transport, vol. 33, no. 1, pp. 22–31, 2018.

XX. T. Phaladisailoed and T. Numnonda, “Machine learning models comparison for Bitcoin price prediction,” in 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE), IEEE, 2018, pp. 506–511.

XXI. V. Derbentsev, N. Datsenko, O. Stepanenko, and V. Bezkorovainyi, “Forecasting cryptocurrency prices time series using machine learning approach,” in SHS Web of Conferences, vol. 65, pp. 02001, 201.

XXII. Y. Peng, P. Albuquerque, J. de Sá, A. Padula, and M. Montenegro, The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression, 2018. Expert Systems with Applications, 97, pp. 177–192.

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