#CurrencyPairPrediction Detecting Currency Shifts with Supervised Models: Detecting currency shifts with supervised models involves training algorithms to identify significant changes or trends in currency values. Key components include: 1. Labeling Shifts: Define currency shifts (e.g., upward/downward movements or volatility spikes) using thresholds or domain rules to create target labels. 2. Feature Engineering: Use indicators like exchange rates, technical indicators (e.g., moving averages, RSI), macroeconomic data, and lagged features. 3. Model Choices: Tree-based models (e.g., Random Forest, XGBoost), neural networks, or logistic regression can classify or predict shifts. 4. Performance Metrics: Use accuracy, precision-recall, or F1-score to evaluate how well the model detects true shifts versus noise. These models help in early detection of market trends, enabling timely trading or hedging decisions.