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As of January 2024, the global cryptocurrency market cap reached $1.66 trillion, marking a 1.5% increase from December 2023. Cryptocurrencies, driven by cryptographic algorithms like SHA-2 and MD5, offer a volatile yet promising investment opportunity. The unpredictable nature of this market has led to the development of various prediction models—such as GRUs, LSTMs, ARIMAX, and others—aimed at forecasting price movements. This study explores a range of machine learning and deep learning algorithms, including SVR, RFR, XGBoost, RNN, CNN, LSTM, the LSTM-GRU hybrid, and fbProphet, to predict cryptocurrency prices with enhanced accuracy. While models like XG- Boost and the LSTM-GRU hybrid demonstrated robust performance, this research also highlights the challenges of overfitting and the importance of data preprocessing and model tuning in improving prediction reliability. The findings provide valuable insights for investors seeking precise forecasts in the dynamic cryptocurrency market.

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Revolutionizing Cryptocurrency Price Prediction: Advanced Insights from Machine Learning, Deep Learning and Hybrid Models

  • 会议论文

  • 发表日期：

    2025年

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  • Abdullah Amberkhani,
  • Harshitha Bolisetty,
  • Ranjith Narasimhaiah,
  • Ghulam Jilani,
  • Betis Baheri,
  • Hanan Muhajab,
  • Areej Muhajab,
  • Kambiz Ghazinour,
  • Safa Shubbar

摘要

As of January 2024, the global cryptocurrency market cap reached $1.66 trillion, marking a 1.5% increase from December 2023. Cryptocurrencies, driven by cryptographic algorithms like SHA-2 and MD5, offer a volatile yet promising investment opportunity. The unpredictable nature of this market has led to the development of various prediction models—such as GRUs, LSTMs, ARIMAX, and others—aimed at forecasting price movements. This study explores a range of machine learning and deep learning algorithms, including SVR, RFR, XGBoost, RNN, CNN, LSTM, the LSTM-GRU hybrid, and fbProphet, to predict cryptocurrency prices with enhanced accuracy. While models like XG- Boost and the LSTM-GRU hybrid demonstrated robust performance, this research also highlights the challenges of overfitting and the importance of data preprocessing and model tuning in improving prediction reliability. The findings provide valuable insights for investors seeking precise forecasts in the dynamic cryptocurrency market.