Integrating Gaussian Processes and Adaptive Boosting for Complex Time Series Forecasting of S&P 500 Index

Abstract

Accurately predicting financial markets remains a significant challenge due to inherent high volatility, nonstationarity, and the difficulty of conventional models in adapting to rapidly changing conditions while simultaneously capturing both subtle trends and abrupt movements. This paper presents a comprehensive methodological framework for predicting S&P 500 price movements by integrating traditional statistical techniques with advanced machine learning methods. We introduce three complementary approaches: an enhanced multi-day forecasting framework that incorporates market condition analysis, a Gaussian Process (GP) forecasting framework with sophisticated uncertainty quantification, and an ensemble forecasting framework that combines multiple methodologies. The enhanced multi-day framework demonstrates strong performance in short-term forecasts with a Mean Absolute Error (MAE) of $35 and a Mean Absolute Percentage Error (MAPE) of 0.89% for forecasts of one day. The Gaussian Process framework exhibits remarkable consistency across different forecasting horizons, achieving a consistent R² value of 0.9962 across the 8-12 day forecast horizon. Integrating Gaussian Process regression with Adaptive Boosting, the ensemble framework achieves superior overall performance with an MAE of $28.30, MAPE of 0.68%, and R² of 0.9789. This research advances the field by introducing robust forecasting methodologies that maintain precision under varying market conditions while providing practical implementation strategies. The key advancement of this research lies in providing a financial forecasting methodology that significantly improves predictive accuracy and offers greater reliability across diverse market conditions, especially in capturing both gradual trends and sudden price shocks, thus overcoming critical limitations inherent in conventional single-model approaches.