A COMPREHENSIVE COMPARATIVE STUDY OF MACHINE LEARNING MODELS FOR PREDICTING CRYPTOCURRENCY


ÜNVAN Y. A., ERGENÇ C.

Facta Universitatis, Series: Electronics and Energetics, vol.37, no.1, pp.211-227, 2024 (ESCI) identifier

  • Publication Type: Article / Article
  • Volume: 37 Issue: 1
  • Publication Date: 2024
  • Doi Number: 10.2298/fuee2401211u
  • Journal Name: Facta Universitatis, Series: Electronics and Energetics
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, zbMATH, Directory of Open Access Journals
  • Page Numbers: pp.211-227
  • Keywords: Cryptocurrencies, Efficiency, Machine Learning Forecasting Model, Prediction methods
  • Ankara Yıldırım Beyazıt University Affiliated: Yes

Abstract

This study aims to find the best performing model in predicting cryptocurrencies using different machine learning models. In our study, an analysis was performed on various cryptocurrencies such as Aave, BinanceCoin, Bitcoin, Cardano, Cosmos, Dogecoin, Ethereum, Solana, Tether, Tron, USDCoin and XRP. Decision Trees, Random Forests, K-Nearest Neighbours (KNN), Gradient Boost Machine (GBM), LightGBM, XGBoost, CatBoost, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Short Term Memory networks in Long Comparisons (LSTM) models were used. The performance of the models is compared with Mean Squared Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The study results show that there is no single model that consistently outperforms others for all cryptocurrencies. Models such as XGBoost and Random Forests show consistent and strong performance across different cryptocurrencies, proving their robustness in this particular use case. Deep learning algorithms, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Long Short Term Memory Networks (LSTMs), show significant accuracy in predicting some cryptocurrencies.