Bet on Features: Anytime-Valid and Feature-Aware Auditing of Conditional Quantile Forecasters
Black-box conditional quantile forecasts are widely used for sequential decisions under asymmetric costs, such as inventory planning in supply chain management. Once deployed, such forecasters must be monitored continuously as data streams drift and regimes change; this invalidates standard, fixed-horizon backtests for calibration. Further, existing backtests do not take into account that the notion of calibration is, in fact, information-dependent: forecasts can look calibrated to an auditor with coarse information while being miscalibrated to an auditor with richer information. We develop a
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 46%Nixtla/statsforecast →
- PossiblePossibly related (embedding) · 45%amazon-science/chronos-forecasting →
- LinkedLinked via arxiv author · 85%Ivane Antonov →
“Bet on Features: Anytime-Valid and Feature-Aware Auditing of Conditional Quantile Forecasters”
- LinkedLinked via arxiv author · 85%Sohom Mukherjee →
“Bet on Features: Anytime-Valid and Feature-Aware Auditing of Conditional Quantile Forecasters”
- LinkedLinked via arxiv author · 85%Richard Pibernik →
“Bet on Features: Anytime-Valid and Feature-Aware Auditing of Conditional Quantile Forecasters”
- LinkedLinked via arxiv author · 85%Yo Joong Choe →
“Bet on Features: Anytime-Valid and Feature-Aware Auditing of Conditional Quantile Forecasters”