2026
From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting
ICLR 2026poster
Most state-of-the-art probabilistic time series forecasting models rely on sampling to represent future uncertainty. However, this paradigm suffers from inherent limitations, such as lacking explicit probabilities, inadequate coverage, and high computational costs. In this work, we introduce **Proba…