Bitcoin Price Trend Forecasting Using Hybrid Deep Learning Models

Bitcoin price forecasting remains a complex and vital task due to the cryptocurrency market’s inherent volatility and nonlinear behavior. This paper presents a robust ensemble-based deep learning framework for predicting Bitcoin price trends. The proposed architecture integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) models to effectively capture temporal dependencies and multidimensional interactions from historical market data. Utilizing a stacking ensemble strategy, the outputs of base models are combined to enhance predictive performance. Key features such as open-high-low-close (OHLC) prices, trading volumes, on-chain metrics, and sentiment indicators are extracted and pre-processed. The model is evaluated using benchmark metrics including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), demonstrating improved accuracy over standalone models. This approach underscores the efficacy of ensemble deep learning in financial time-series forecasting, offering valuable insights for traders and institutions navigating dynamic crypto markets.