Cryptocurrency Forecasting: A Comparative Study of Machine Learning and Deep Learning Approaches
DOI:
https://doi.org/10.65405/76e4gv75Keywords:
Cryptocurrencies, Machine Learning, Deep Learning, Bitcoin, Dogecoin, Ethereum, USDCoin, BinanceCoin, CardanocoinAbstract
In today’s financial landscape, understanding and predicting cryptocurrency trends are of utmost importance due to the significant growth of digital currencies. This paper delves into a comprehensive study of predictive modeling for six major cryptocurrencies: Bitcoin, Dogecoin, Ethereum, USDCoin, BinanceCoin, and Cardanocoin. Using a wide range of machine learning and deep learning algorithms, we carefully assess how well each model can capture the complex nature of cryptocurrency markets. We employed a rigorous methodology, including models like Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and eXtreme Gradient Boosting (XGBoost), among others. We evaluated each model’s performance using standard metrics like RMSE and R². The results show that different models perform differently, with some algorithms excelling in predicting specific cryptocurrencies.
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