Demand forecasting and quantification of uncertainty is an essential asset of the retail supply chain optimization and risk-based inventory decisions. This study will introduce a new hybrid conditional variance model (combining gradient boosting machines (XGBoost, LightGBM), recurrent neural...

Background: Accurate inventory management of intermittent spare parts requires precise demand forecasting. The sporadic and irregular nature of demand, characterized by long intervals between occurrences, results in a significant data imbalance, where demand events are vastly outnumbered by...

This study examines the impact of Empirical Mode Decomposition (EMD) and Recursive Feature Elimination (RFE) on the prediction of financial product performance employing several ensemble machine learning models, including Random Forest, XGBoost, LightGBM, AdaBoost, CatBoost, Bagging, and...