#AIImpactOnForex AI Hybrid Models for Currency Movement Prediction AI hybrid models combine multiple machine learning (ML) and deep learning (DL) techniques to improve the accuracy of predicting currency (forex) movements. Traditional single models like neural networks or decision trees often struggle with the complex, noisy, and highly nonlinear nature of forex markets. Hybrid models address this by integrating different strengths — for example, combining statistical models (like ARIMA) with AI methods (like LSTM networks), or blending feature engineering (e.g., technical indicators) with ensemble learning (e.g., Random Forest + Gradient Boosting). Common hybrid strategies include: Model Fusion: Merging outputs from several models to make a more robust prediction. Sequential Hybridization: Using one model for feature extraction and another for prediction. Optimization-Based Hybridization: Enhancing models using optimization algorithms like Genetic Algorithms or Particle Swarm Optimization. Hybrid models often lead to improved forecasting accuracy, better generalization to new market conditions, and greater resilience against market volatility.