22/7/2025

 22/7/2025

Today's work focuses on further optimizing the XGBoost model, especially conducting more dimensional exploration and experiments in the feature engineering stage, in order to further reduce the prediction error (MAPE) and improve the generalization ability of the model. I tested and adjusted the expression of periodic changes. The original model only used time fields such as "day" and "month" as categorical features, but this processing method is not sensitive enough to the potential periodicity in the time series (such as the cyclicity of the month or the difference in patterns on different days of the week). Therefore, I began to try to add sine and cosine (sin/cos) conversion to represent periodicity.