Predicting Bitcoin’s Price: A Critical Review of Forecasting Models and Methods

The emergence and rapid development of cryptocurrencies and their growing influence on financial markets have prompted extensive research to increase the ability to predict prices accurately. This article thoroughly reviews the different methods applied in this field. Our analysis includes traditional statistical methods, time series analysis, neural networks, and hybrid methods. Advanced models, such as Long Short-Term Memory (LSTM) networks and Reinforcement Learning, are acknowledged for their potential, albeit with notable computational and data demands. The discussion also addresses the challenges posed by the speculative and highly volatile nature of the cryptocurrency market, which can lead to rapid market shifts and affect model performance. Finally, we propose future research directions, including the development of hybrid models that integrate multiple prediction techniques and applying Auto Machine Learning (AutoML) to create more suitable models with adaptive capabilities for market fluctuations. This comprehensive review aims to shed light on the current landscape of cryptocurrency prediction research and highlight potential pathways for further advancements in this evolving field.