数据稀缺时，提升罕见事件预测覆盖率的关键是校准集多样性。
Reaching the Tail: Calibration Diversity Drives Conformal Coverage under Data Scarcity
- 通过最大化校准集多样性，改进罕见事件的覆盖效果。
- 六个月内覆盖率从67.8%提升至81.4%，显著优于其他方法。
- 适合关注长期罕见事件预测与不确定性量化研究者。
在长期宏观经济序列数据稀缺的背景下，多时间尺度罕见事件预测面临挑战：标签事件稀少，且标准不确定性量化假设交换性，但自相关性破坏了该假设。受控消融实验表明，自适应共形推断中的罕见事件阈值实际上反映的是校准集大小。在200个随机校准集中，非符合度分数分布的支持宽度解释了高达85%的覆盖率方差，而罕见事件数量仅解释2%；这一结果在合成数据和五个国家中一致重现（五国斯皮尔曼ρ为0.45–0.66，远高于罕见事件计数的0.02–0.23）。基于此构建的多样性最大化选择器是唯一能提升长期覆盖率的方法（六个月从67.8%增至81.4%），而Mondrian、移位鲁棒及极值方法均未改善，甚至在使用最优标签时，Mondrian反而恶化覆盖率。紧凑模型解释其原因：覆盖率缺口源于校准集上分位数与测试分布的接近程度。多样性必要但不充分。该方法在基于RegressorChain的美国衰退预测框架中验证，六月覆盖率能否达90%仍开放，本文将其量化而非解决。
原文摘要 · Abstract (English)
Multi-horizon rare-event forecasting is hard under long macroeconomic series' data constraints: labeled events are scarce, and standard uncertainty quantification assumes an exchangeability that autocorrelation violates. A controlled ablation shows an apparent rare-event threshold for Adaptive Conformal Inference instead reflects calibration-set size. Across 200 random calibration sets, support width of the nonconformity-score distribution explains up to 85% of coverage variance versus 2% for rare-event count; the same, not the same magnitude, replicates across synthetic conditions and five countries (five-country Spearman $ρ$ 0.45-0.66 vs. 0.02-0.23). A diversity-maximizing selector built on this is the only strategy tested that improves long-horizon coverage (67.8% to 81.4% at six months); Mondrian, shift-robust, and extreme-value alternatives fail to close it. Mondrian even worsens coverage under oracle labels. A compact proposition explains why: coverage deficit reflects how closely the calibration set's upper quantile reaches the test distribution's. Diversity is necessary, not sufficient. Demonstrated on a two-stage U.S. recession-forecasting framework with RegressorChain, whether six-month coverage reaches 90% under honest scoring remains open, a question this paper quantifies rather than resolves.
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