Forecasting Bitcoin Price Prediction with Long Short-Term Memory Networks: Implementation and Applications Using Streamlit

Abstract

The rapid growth of cryptocurrency markets, particularly Bitcoin, has highlighted the need for accurate price prediction models to support informed decision-making. While existing studies primarily evaluate machine learning models for price forecasting, few have implemented these models in real-world applications. This paper addresses this gap by developing a Bitcoin price prediction system using Long Short-Term Memory (LSTM) networks, integrated into a user-friendly web-based application powered by Streamlit. The model forecasts Bitcoin prices at 5-minute, 1-hour, and 1-day intervals, demonstrating strong predictive performance. For the 5-minute interval, the model achieved a Mean Squared Error (MSE) of 53,479.86, Mean Absolute Error (MAE) of 150.58, Root Mean Squared Error (RMSE) of 231.26, and Mean Absolute Percentage Error (MAPE) of 0.144%. At the 1-hour interval, errors increased moderately with an MSE of 423,198.24, MAE of 499.93, RMSE of 650.54, and MAPE of 0.505%. For the 1-day interval, the model faced greater variability, reflected in an MSE of 3,089,699.07, MAE of 1,058.88, RMSE of 1,757.75, and MAPE of 2.027%. These results indicate that while predictive precision decreases over longer horizons, the model maintains strong performance across all timeframes. By embedding LSTM predictions into an interactive, real-time forecasting platform, this study demonstrates the practical integration of deep learning into complex financial systems. Beyond cryptocurrency, the approach highlights the potential of intelligent computational models to enhance decision-making processes in data-intensive domains, reinforcing the role of informatics in bridging advanced algorithms with usable technological solutions.