#AIImpactOnForex Building a Predictive Model for Currency Movements Using RNNs To predict currency movements, Recurrent Neural Networks (RNNs) are effective because they capture temporal dependencies in time series data. The process involves several key steps: 1. Data Collection and Preprocessing: Gather historical exchange rate data along with relevant features like interest rates, inflation, and market indicators. Normalize or scale the data to improve model performance. 2. Feature Engineering: Create lagged features, moving averages, and volatility measures to help the model understand trends and patterns. 3. Model Design: Build an RNN architecture — often a Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) network — to address issues like vanishing gradients and to better capture long-term dependencies. 4. Training: Split the data into training, validation, and testing sets. Use loss functions like Mean Squared Error (MSE) and optimizers like Adam. Regularization techniques (like dropout) can help prevent overfitting. 5. Evaluation and Tuning: Assess the model using metrics such as RMSE or directional accuracy (whether the model predicts the direction correctly). Hyperparameter tuning (e.g., number of layers, hidden units, learning rate) improves performance. 6. Deployment: Once validated, the model can be integrated into trading systems or used