Predictive Surprise as Self-Grounding Concept Bottleneck for Interpretable Time Series
7 Nov 2026
Time-series classifiers deployed in risk-sensitive domains must be (1) accurate, (2) interpretable, and (3) able to abstain when evidence is weak. Existing methods do not deliver all 3 at once, and the obstacle is structural, rather than logistical.