Evaluating Ensemble Methods in Big Data Analytics for Real-Time Financial Forecasting

This research investigates the efficacy of various ensemble methods in financial forecasting bycomparing Random Forests, Gradient Boosting, XGBoost, and AdaBoost. With the advent of bigdata analytics, accurate financial predictions are crucial for informed decision-making in volatilemarkets. This study evaluates the performance of these ensemble techniques using a range of metricsincluding accuracy, precision, recall, F1 Score, Mean Absolute Error (MAE), and Root Mean SquaredError (RMSE). Experimental results reveal that XGBoost outperforms other methods with the highestaccuracy (89%) and lowest error metrics, demonstrating its superior capability in handling complexfinancial data. Gradient Boosting follows closely with robust performance and balanced metrics, whileRandom Forests and AdaBoost, though less effective, still provide valuable predictive insights. Thefindings underscore the significant advancements that ensemble methods bring to financial forecastingand highlight XGBoost as the most reliable approach for achieving accurate and precise predictions indynamic financial environments..