Feature Selection for Rare Earth Element Price Forecasting - A Hybrid Econometric and Machine Learning Approach
This thesis addresses the need for robust price forecasting in the volatile Rare Earth Elements (REEs) markets. With a specific focus on neodymium, given it is the most utilized REEs, we investigate how feature selection can be effectively performed to guide REE price forecasting by proposing and evaluating three distinct methodologies: Adaptive Lasso, Post-Double Selection (PDS), and an integrate
