Efficient Bitcoin Price Forecasting Using Deep Learning and Ensemble Methods

Bitcoin has emerged as one of the most volatile and widely traded digital assets, making accurate price forecasting both challenging and essential for investors and researchers. The aim of this project is to design and implement a forecasting system that predicts Bitcoin prices using a combination of machine learning and time-series automation techniques. The system is built using the AutoTS library, which automatically evaluates multiple forecasting models and generates a 7-day price forecast. To enhance interpretability, a Random Forest regressor is integrated to identify the relative importance of technical indicators such as daily returns, moving averages, volatility, and volume change. Together, these models provide both predictive accuracy and insights into market behavior. The application has been developed as a Tkinter-based desktop interface, allowing users to input custom date ranges, fetch historical data directly from the Binance API, and visualize forecasts alongside actual price trends. The system also supports exporting results into CSV and Excel formats for further analysis. Evaluation metrics including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) are used to assess forecast quality. This work demonstrates the potential of combining automated time-series modeling with ensemble learning to capture complex financial patterns. While the system performs effectively for short-term forecasting,