#CurrencyPairPrediction Currency Correlation Features in Supervised Learning: Currency correlation features capture the relationship between different currency pairs and can enhance predictive models in finance. These features are especially useful in tasks like forex prediction, risk modeling, and portfolio optimization. Key aspects include: 1. Correlation Metrics: Pearson or Spearman correlation coefficients measure the linear or rank-based relationship between currency pairs. 2. Rolling Correlation: Calculates correlations over moving time windows to capture dynamic relationships over time. 3. Feature Engineering: Correlation values can be used directly or transformed into signals (e.g., divergence from historical norms). 4. Lagged Correlations: Introduce time-shifted correlations to uncover leading or lagging relationships between currencies. These features help supervised learning models better understand market behavior and improve forecasting performance.