Comparative Analysis of LightGBM and Random Forest for Daily Bitcoin Closing Price Prediction with Ensemble Approach - Nolejanduma, Dionisius Nusaca Redegnosis; Rahardi, Majid (Journal of Applied Informatics and Computing, 2025)
This study performs a comparative analysis of the LightGBM and Random Forest algorithms in predicting daily Bitcoin closing prices, with an exploration of an Ensemble approach for potential improvements in accuracy. A quantitative research design is employed, utilizing historical Bitcoin (BTC-USD) data from September 2015 to July 2025, enriched with various technical indicators. Data preprocessing, model training, and evaluation were carried out using Python in Google Colaboratory, with the dataset split into 80% for training and 20% for testing. Model performance was evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the R-squared (R²) statistic, with statistical significance tests to ensure robust comparisons. A simple Linear Regression model was also included as a baseline. The findings reveal that Random Forest outperformed LightGBM, achieving an MAE of 11,599.74, an RMSE of 19,262.31, and an R² of 0.431, compared to LightGBM’s MAE of 12,285.42, RMSE of 19,995.04, and R² of 0.386. Although the Ensemble model showed slight improvements over LightGBM, it did not surpass Random Forest. The relatively low R² values across all models reflect the inherent volatility and difficulty in predicting Bitcoin prices. The study concludes that Random Forest demonstrates superior robustness for Bitcoin forecasting. Importantly, this work provides a novel empirical contribution by being one of the first to directly benchmark RF, LightGBM, and their Ensemble for Bitcoin prediction, highlighting that a simple averaging Ensemble does not guarantee superior performance. This finding provides a foundation for developing more refined Ensemble strategies tailored to high-volatility assets.