面向多分布偏移下可靠经济预测区间的动态机制感知共形校准

中文摘要 AI 辅助

共形预测提供了与分布无关的预测区间，但依赖于可交换性假设，而在经济预测中，该假设常因协变量偏移、概念漂移、局部异质性和潜在机制而被违背。我们提出动态机制感知共形预测（Dynamic Regime-Aware Conformal Prediction, DRACP），其在统一加权共形校准框架中结合了密度比、局部核、概率机制感知加权与自调节在线显著性控制器。我们得到三项理论结果：在先验重要性权重下的有限样本有效性、针对估计权重的覆盖差距边界（速率与有效样本大小相关），以及在线控制器的确定性或遗憾保证。我们在48个真实预测序列上，将DRACP与6种基线方法进行评估，这些序列涵盖欧元区和欧盟27国HICP通胀、美国宏观经济与能源指标及每日金融序列。近期在线方法（FACI、强自适应在线共形预测和共形PID）基于作者实现进行验证。DRACP并非最高效的方法：强自适应在线共形预测取得最佳区间得分，区间宽度约窄20%；相反，DRACP提供最可靠的校准，其覆盖率最接近名义值0.90（0.890），在所有序列上从未低于0.80，在所有预测区间保持最佳覆盖率，并在2021-2023年通胀激增期间表现最佳。强自适应方法在48个序列中有20个存在覆盖不足，而DRACP仅为10个。因此，DRACP在校准与效率间提供了原则性权衡，当预测区间必须满足覆盖率标准时，它更倾向于可靠覆盖。消融研究显示，在线控制器和条件尺度归一化贡献了大部分性能提升，而加权组件的贡献较小。

英文摘要

Conformal prediction provides distribution-free prediction intervals but relies on exchangeability, an assumption often violated in economic forecasting because of covariate shift, concept drift, local heterogeneity and latent regimes. We propose Dynamic Regime-Aware Conformal Prediction (DRACP), which combines density-ratio, localized kernel and probabilistic regime-aware weighting with a self-tuning online significance controller in a unified weighted conformal calibration framework. We distinguish three theoretical results: finite-sample validity under oracle importance weights, a coverage-gap bound for estimated weights with rates in effective sample size, and deterministic or regret guarantees for the online controller. We evaluate DRACP against six baselines on 48 real forecasting series covering euro-area and EU-27 HICP inflation, US macroeconomic and energy indicators, and daily financial series. Recent online methods (FACI, strongly-adaptive online conformal prediction and conformal PID) were verified against the authors' implementations. DRACP is not the most efficient method: strongly-adaptive online conformal prediction achieves the best interval score and intervals about 20% narrower. Instead, DRACP provides the most reliable calibration, achieving coverage closest to the nominal 0.90 (0.890), never falling below 0.80 on any series, maintaining the best coverage at all forecast horizons, and performing best during the 2021-2023 inflation surge. The strongly-adaptive method undercovers on 20 of 48 series versus 10 for DRACP. DRACP therefore offers a principled trade-off between calibration and efficiency, favoring reliable coverage when prediction intervals must satisfy coverage standards. An ablation study shows that the online controller and conditional-scale normalization provide most of the performance gain, whereas the weighting components make a smaller contribution.

发表机构

• University of Bucharest（布加勒斯特大学）
• National Institute of Research and Development for Biological Sciences（国家生物科学研究与发展研究所）

机构由 AI 辅助整理，请以论文原文为准。