#CurrencyPairPrediction Incorporating currency event data into models involves integrating information about economic events, geopolitical developments, and central bank decisions that can impact currency markets. These events are often used as features in predictive models for forex forecasting or risk assessment. Data sources include economic calendars, news feeds, and sentiment analysis. Techniques such as time-series alignment, event labeling, and feature engineering (e.g., encoding surprise factors or volatility impact) are used to structure the data. Models like ARIMA, LSTM, or XGBoost can then leverage this enriched dataset to improve forecasting accuracy and responsiveness to real-world events.