Abstract

This paper focuses on assessing the accuracy of predicting the direction of Bitcoin's price in USD. Historical price data is extracted from the Bitcoin Price Index, and the task is approached with varying levels of success by leveraging Bayesian-optimized Support Vector Regression (SVR) methods and Long Short-Term Memory (LSTM) networks. Among the methods employed, LSTM achieves the highest classification accuracy at 52% and a Root Mean Square Error (RMSE) of 8%. In addition, the popular ARIMA model for time series forecasting is incorporated for comparison with machine learning models. As anticipated, the non-linear machine learning approaches outperform ARIMA, which exhibits poor performance. Lastly, the study includes a benchmarking analysis of both machine learning models implemented on both a GPU and a CPU, revealing a 67.7% improvement in training time for the GPU-based implementation.

Description