A comparison between Super Vector Regression, Random Forest Regressor, LSTM, and GRU in Forecasting Bitcoin Price


  • Rifando Panggabean Politeknik Caltex Riau
  • Yohana Dewi Lulu Widyasari Politeknik Caltex Riau


Machine Learning, Bitcoin, Cryptocurrency, LSTM, SVR, GRU, RF


High bitcoin user volume results in high market volatility, and indicators commonly used in

stock and forex transactions have low accuracy in handling bitcoin's highly volatile market. The present

study aims to find out the most optimal machine learning algorithm for Bitcoin transactions by examining

four algorithms: Super vector regression(SVR),Random Forest Regressor(RF),Long short-term

memory(LSTM), and Gated Recurrent Unit (GRU), examined using four tests, namely Root Mean Square

Error (RMSE), Mean Square Error (MSE) , Mean Absolute Error (MAE) and R-Squared(R2). The test was

performed using Bitcoin data between 2014 and 2022. The test result showed that LSTM+GRU algorithm

exhibited the highest accuracy, indicated by a R-squared of 94%.