What question did this study set out to answer?

The aim is to evaluate different machine learning models for predicting cryptocurrency prices based on volatility.

March 23, 2026Open Access

A Comparative Study of Machine Learning Models on Cryptocurrency Prices

DČDušan ČatlochTechnical University of Košice ECEva ChovancováTechnical University of Košice

Key Points

The aim is to evaluate different machine learning models for predicting cryptocurrency prices based on volatility.
Compared traditional machine learning (Linear Regression, SVR) with ensemble methods (Random Forest, Gradient Boosting) and deep learning (LSTM, GRU).
Utilized historical price data from CoinGecko.
Applied a sliding window technique and normalization for data preparation.
Assessed model performance using Root Mean Square Error (RMSE) and relative percentage error.
LSTM and GRU models achieved the highest predictive accuracy.
Linear Regression performed well for stable cryptocurrencies like BTC, ADA, and DOT.
Ensemble methods showed moderate performance, while SVR consistently underperformed.

Abstract

Abstract The volatility of cryptocurrencies poses challenges for accurate price forecasting. This study compares traditional machine learning models (Linear Regression, Support Vector Regression), ensemble methods (Random Forest, Gradient Boosting), and deep learning architectures (LSTM, GRU) in predicting the daily prices of Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), Ripple (XRP), and Polkadot (DOT). Using historical data from CoinGecko, a sliding window, and normalisation, we assess models by Root Mean Square Error (RMSE) and relative percentage error. Results show that LSTM and GRU achieve the best overall accuracy, while Linear Regression remains competitive for stable assets such as BTC, ADA, and DOT. Ensemble methods performed moderately, whereas SVR consistently underperformed. The findings underline the importance of matching prediction models to the characteristics of specific cryptocurrencies.

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