面向非可交换面板数据的在线共形预测：W-TQA 方法

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Abstract: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 the weights under a profile-similarity assumption, and show that the implemented procedure, which falls back on the largest peer score, attains long-run average coverage under missing-completely-at-random feedback and a feasibility condition on the calibration panel. On synthetic panels and four real panels, including a genuinely asynchronous U.S.-to-Tokyo equity panel, W-TQA attains the highest coverage on the worst-covered target units on every panel; similarity weighting contributes most when target feedback is scarce, and temporal adaptation more as feedback accumulates.

Comments:	45 pages, 4 figures
Subjects:	Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
Cite as:	arXiv:2605.17705 [stat.ML]
	(or arXiv:2605.17705v2 [stat.ML] for this version)
	https://doi.org/10.48550/arXiv.2605.17705 arXiv-issued DOI via DataCite

Submission history

From: Daohong Tu [view email]
[v1] Mon, 18 May 2026 00:02:11 UTC (459 KB)
[v2] Mon, 5 Oct 2026 22:02:05 UTC (610 KB)