A Comparative Study of Machine Learning Approaches for Predicting Close-Price Cryptocurrency

Abstract

This study aimed to evaluate the effectiveness of several algorithms for predicting the close-price of various cryptocurrencies. Three algorithms employed in this comparative study were Support Vector Regression (SVR), Random Forest (RF), and Long Short-Term Memory (LSTM), while the three cryptocurrency datasets examined were Bitcoin, Ethereum, and Litecoin. Furthermore, in the stage of the data preparation, we compared two popular data normalization methods: min-max and z-score. After examining the close-price prediction results of each approach using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), it was revealed that the predictive model generated by the LSTM algorithm together with z-score normalization yielded the most effective results for each cryptocurrency dataset.