Abstract:
The global surge in cryptocurrency markets, particularly Bitcoin, has generated a growing demand for accurate and reliable price prediction models. Traditional forecasting methods, while helpful, have demonstrated certain limitations in terms of accuracy and adaptability. This study aims to challenge the status quo by advancing Bitcoin price prediction through the application of innovative machine learning techniques.
The research investigates the current state of Bitcoin price forecasting, evaluating the performance of conventional models and identifying their weaknesses. Furthermore, the study explores a wide range of machine learning algorithms, including regression techniques, time series analysis, deep learning models, and ensemble methods, to improve the existing prediction strategies. The selection of these algorithms is based on their potential to enhance the accuracy, robustness, and adaptability of the models.
Using a comprehensive dataset of historical Bitcoin prices, market indicators, relevant macroeconomic factors, and sentiment analysis data on the crypto market, the study conducts a comparative analysis of the selected machine learning techniques. By applying rigorous model evaluation criteria, the research highlights the most promising approaches for superior Bitcoin price prediction. Additionally, the study delves into the interpretability of these models, emphasizing the importance of understanding the underlying factors that drive price changes.
The best-performing model is then utilized to backtest simple trading strategies, providing valuable insights into the practical application of the proposed prediction techniques in the context of cryptocurrency trading. The inclusion of sentiment analysis data in a second scenario further extends the understanding of the complex interplay between market sentiment and price fluctuations, offering a more comprehensive perspective on the drivers of Bitcoin price movements.
