Bitcoin Price Forecasting using Seasonal Log-Differenced XGBoost with 2014–2025 Data - Akbar, Muhammmad (Sistemasi: Jurnal Sistem Informasi, 2025)
Bitcoin, as a decentralized digital currency, experiences significant price fluctuations, making accurate price forecasting a complex yet valuable challenge. Price forecasting is essential in economic decision-making, serving as the foundation for portfolio construction, risk analysis, and investment strategy development. Bitcoin's high volatility makes it an attractive asset for investors but also poses significant risks, necessitating sophisticated forecasting tools and models to mitigate uncertainty. The XGBoost model in regression is widely known and effectively applied to handle time series data. This model can capture complex nonlinear relationships in Bitcoin price data, providing more accurate forecasts than traditional statistical models. The research methodology includes data collection, data preprocessing, stationarity checking, differencing, feature engineering, data division into training and testing sets, XGBoost model training, prediction and evaluation, and result visualization. The research results show that the XGBoost model achieves a Mean Absolute Error of 8.26% and an RMSE of 9.87%, indicating excellent forecasting accuracy. The implications of this research could potentially assist investors and traders in improving their strategies and risk management.