Online Conformal Prediction for Non-Exchangeable Panel Data
We study online conformal prediction in a partially observed panel: a new cross-section of peer outcomes is observed before each target outcome, target feedback may be intermittent or absent, and neither units nor rounds need be exchangeable. We propose Weighted Temporal Quantile Adjustment (W-TQA), which combines similarity weights learned from unit histories with an adaptive target-specific miscoverage level. We prove that neither target-peer mismatch nor coverage on unrevealed rounds is identifiable, so assumptions on cross-unit similarity and on the feedback mechanism cannot be avoided. We bound the past-conditional miscoverage in terms of this mismatch, quantify the cost of learning ...
| Authors | Daohong Tu, Kay Giesecke |
|---|---|
| Primary category | stat.ML |
| Published | 18 May 2026 |
| Last updated at source | 5 October 2026 |
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