A Comparative Study of Regression Analysis for Modelling and Prediction of Bitcoin Price

Abstract

AbstractThe appraisal of Bitcoin’s price-changing characteristics is extremely difficult because of the nonlinear, nonstationary, effect of multiple uncontrollable factors, and volatile nature. The conventional approaches to machine learning categorization failed to yield accurate results. Additionally, the performance of the presented regression model strategies was evaluated in terms of the mean absolute percentage error (MAPE) between predicted actual values and expected values, as well as the root mean square error (RMSE). These two performance indicators are insufficient to demonstrate the efficacy of Bitcoin price prediction. The aim of this paper is to propose six regression models for Bitcoin price prediction based on historical data from 2014 to 2020. We employ six different regression models. They are CatBoost regressor, gradient boosting regressor, extra tree regressor, AdaBoost regressor, K-nearest neighbor regressor, and the Theil-Sen regressor. Coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), root mean squared logarithmic error (RMSLE), and mean absolute percentage error (MAPE) were used to examine the models’ performance. The experimental results indicated that the extra tree had the highest MAE of 3.1447, RMSLE of 0.553, and MAPE of 0.4783 while the gradient boosting regressor had the highest MSE of 5.4842 and RMSE of 7.4055. Theil-Sen regressor model produced the highest R2 value of 0.4533.KeywordsBitcoin priceCatBoost regressorGradient boosting regressorExtra tree regressorTheil-Sen regressorCoefficient of determinationMean absolute percentage error