#CurrencyPairPrediction Impact of Feature Lagging on Prediction Accuracy Feature lagging involves using past values of variables (lags) as inputs to predict future outcomes, which is common in time-series models like those used in forex: Captures Temporal Patterns: Lagged features help models recognize trends, cycles, and momentum in price movements. Prevents Data Leakage: By only using past data, lagging ensures the model doesn’t inadvertently use future information. Improves Accuracy: Properly selected lags can enhance predictive power by providing relevant historical context. Risk of Redundancy: Too many or irrelevant lagged features can lead to overfitting or dilute important signals. When applied thoughtfully, feature lagging strengthens the model’s temporal awareness and improves forecasting accuracy in financial time series.