#AIImpactOnForex Tuning AI models for currency forecasting involves optimizing machine learning algorithms to predict currency exchange rates. Key techniques include: 1. Feature Selection: Identifying relevant economic indicators, historical data, and market sentiment to improve model accuracy. 2. Model Selection: Using algorithms such as neural networks, decision trees, and support vector machines (SVM) that can capture complex patterns in financial data. 3. Hyperparameter Optimization: Adjusting parameters like learning rate, regularization strength, and network layers to enhance model performance. 4. Data Preprocessing: Handling missing data, scaling features, and removing noise to ensure the model works with high-quality input. 5. Evaluation Metrics: Using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to assess prediction accuracy. 6. Backtesting: Testing the model on historical data to evaluate its performance before live deployment. Tuning requires a balance of computational resources, expert insights into financial markets, and iterative refinement to achieve reliable forecasting results.