Forecasting Bitcoin Prices: A Comparative Study of Machine Learning and Deep Learning Algorithms

Abstract The cryptocurrency market, particularly Bitcoin, has witnessed significant volatility, making accurate price prediction a challenging yet crucial task. This research explores the application of four powerful machine learning algorithms), Light Gradient Boosting Machine (LightGBM , Long Short Term Memory (LSTM), Bidirectional Long Short Term Memory (BiLSTM) and Extreme Gradient Boosting (XGBoost), for forecasting Bitcoin prices. The study focuses on evaluating the predictive performance using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) as the evaluation metrics. The LSTM and Bi-LSTM, a type of recurrent neural network (RNN), are known for that ability to capture long-term dependencies in time series data. On the other hand, LightGBM and XGBoost, a gradient boosting framework, excels in handling large datasets efficiently and delivering accurate predictions. By employing these algorithms, this research aims to enhance the accuracy of Bitcoin price predictions compared to traditional methods. The experimental setup involves training and validating the models on historical Bitcoin price data. The MAE and RMSE metrics are utilized to assess the models' predictive accuracy, providing a comprehensive evaluation of their performance. The comparative analysis of machine learning models sheds light on their strengths and weaknesses in the context of cryptocurrency price prediction. The results showcase the importance of employing advanced machine learning techniques in forecasting financial time series, highlighting the potential for improved decision-making in cryptocurrency trading and investment strategies.