ICASSP 2026 - CCF B
Towards Reliable Time Series Forecasting under Future Uncertainty
Ambiguity and novelty rejection for forecasting
TL;DR
This work improves forecasting reliability by rejecting ambiguous or distribution-shifted predictions before deployment risk grows.
Abstract
Forecasting systems often fail because future uncertainty is not visible at training time. This work combines ambiguity rejection and novelty rejection to identify low-confidence or out-of-distribution future states, allowing the model to abstain from unreliable predictions when future labels are unavailable.
Key Idea
- Uses ambiguity rejection to abstain under high predictive uncertainty.
- Uses novelty rejection to detect distribution-shifted future conditions.
- Targets deployment settings where future ground truth is unavailable.
Poster Figure
Overview of ambiguity and novelty rejection for reliable time-series forecasting.