#AIImpactOnForex Sure! Here's a short summary: Ensemble Methods for Forex Rate Forecasting Ensemble methods combine multiple models to improve the accuracy and robustness of foreign exchange (forex) rate forecasting. In volatile forex markets, single models often struggle with noise and nonlinearity. Ensemble techniques like Bagging, Boosting, and Stacking help by leveraging the strengths of diverse algorithms. Bagging (e.g., Random Forests) reduces variance by averaging predictions from many models trained on different data subsets. Boosting (e.g., XGBoost, AdaBoost) reduces bias by sequentially training models that focus on correcting previous errors. Stacking blends different model types (like neural networks, SVMs, and decision trees) and uses a meta-model to learn how to best combine them. By aggregating multiple predictions, ensemble methods typically outperform individual models, providing more stable and accurate forex forecasts.