Horizon-Constrained Rashomon Sets for Chaotic Forecasting

This research introduces horizon-constrained Rashomon sets, a theoretical framework bridging predictive multiplicity and chaotic dynamics in machine learning. It demonstrates how model multiplicity evolves with prediction horizon in chaotic systems, proving exponential contraction of the effective Rashomon set with lead time.