重叠多步预测的贝叶斯协调：跨来源一致性的后处理框架
Bayesian Reconciliation of Overlapping Multi-Step Forecasts: A Post-Processing Framework for Cross-Origin Coherence
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中文总结 AI 辅助
针对多步预测中跨来源不一致性问题，提出模型无关的贝叶斯后处理协调框架，通过状态空间模型联合校准重叠预测，显著降低不一致性并提升区间覆盖率。
中文摘要 AI 辅助
多步预测系统，包括现代人工智能（AI）模型，通常从不同的预测起点对相同的未来时间点发布重叠预测。即使由同一模型生成，这些预测也可能存在分歧，我们将这种现象称为跨来源不一致性。我们提出了一种贝叶斯协调框架，将重叠预测视为对共同潜在轨迹的带噪声且可能带有偏差的测量。该方法作为模型无关的后处理层运行，无需访问预测模型的架构、参数或训练数据。一个贝叶斯状态空间模型联合协调针对相同未来时间的预测，同时考虑视界特定的偏差和不确定性。受控模拟表明，在假设的数据生成过程下，跨来源不一致性显著降低，且潜在轨迹恢复准确。对每日标普500波动率预测的实证应用将90%预测区间覆盖率从94.8%降至88.3%，更接近名义覆盖率，但点预测误差有所增加。额外的LSTM实验证明了该方法对现代神经预测系统的适用性。
英文摘要
Multi-step forecasting systems, including modern artificial intelligence (AI) models, often issue overlapping predictions for the same future time from different forecast origins. These predictions can disagree even when produced by the same model, a phenomenon we call cross-origin incoherence. We propose a Bayesian reconciliation framework that treats overlapping forecasts as noisy and potentially biased measurements of a common latent trajectory. The method operates as a model agnostic post-processing layer, requiring no access to the forecasting models architecture, parameters, or training data. A Bayesian state-space model jointly reconciles forecasts targeting the same future time while accounting for horizon-specific bias and uncertainty. Controlled simulations demonstrate substantial reductions in cross-origin incoherence and accurate latent trajectory recovery under the assumed data generating process. An empirical application to daily S and P 500 volatility forecasts reduces 90% prediction interval coverage from 94.8% to 88.3%, closer to nominal coverage, but increases point forecast error. An additional LSTM experiment demonstrates applicability to modern neural forecasting systems.
发表机构
- University of São Paulo（圣保罗大学）
机构由 AI 辅助整理，请以论文原文为准。
