IPL Polynomial Degree Limits

Background: Deep learning and other complex forecasting models often achieve high predictive accuracy but lack transparency regarding how specific input features influence the output.

Question / Future Work: Further investigation is required to explore the limits of polynomial degree selection (s) in the Interpretable Polynomial Learning (IPL) model, specifically how the trade-off between prediction accuracy and interpretability changes when using degrees beyond the $s=2$ explored in the simulated data analysis.