Bitcoin Price Forecasting: A Comparative Study of Machine Learning, Statistical and Deep Learning Models

Neelam Urooj ()
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Neelam Urooj: Institute of Business Management and Administrative Sciences(IBM & AS), The Islamia University of Bahawalpur,Bahawalpur, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 2, 396-412

Abstract: Introduction/Importance of Study:Cryptocurrency price prediction is crucial for investors and researchers, given the market's nonlinear nature and the potential for significant financial implications. Novelty:This study offers a novel approach to cryptocurrency price prediction, leveraging a range of machine learning and deep learning models to address the challenges of predicting Bitcoin's exchange rate. Materials & Methods:The study employs various machine learning and deep learning models, including Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), along with traditional models like Linear Regressor, Random Regressor, ExtraTreesClassifier, XGBoost Regressor, ARIMA, Prophet, and CNN. Results & Discussion:The ExtraTreesClassifier model emerged as the top performer, achieving a Test MAPE of 0.0689. This model outperformed deep learning models like RNNs, indicating its effectiveness in cryptocurrency price prediction. Conclusion:The findings suggest that the proposed models, particularly the ExtraTreesClassifier, can provide valuable insights for investors and traders in the cryptocurrency market.

Keywords: Bitcoin; Forecasting; Statistical Analysis; Machine Learning; Deep Learning (search for similar items in EconPapers)
Date: 2024
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