理解数据修订的时间序列基础模型

中文摘要 AI 辅助

历史观测值并非总是固定的：随着新证据的出现，统计机构会修订先前发布的值。因此，从当代下载的数据进行预测，可能会使模型接触到在其声称做出预测的日期尚不可用的信息。我们提出VINTAGE-TS，一种对时间序列基础模型的修订感知适配，它区分了观测时间与信息可用时间。其目标是下一期的首次发布值以及该发布后固定天数内可用的值；两者均不被声明为最终真相。联合预测分布保留了这些目标之间的依赖性，并暴露了它们差异的不确定性。我们指定了基于ALFRED的滚动评估、匹配的Chronos-2比较、常规和修订感知基线，以及一项单独的预训练重叠审计。随附软件实现了有效性区间重建、延迟标签过滤、冻结骨干适配器接口和可复现的诊断。一个已执行的合成演示和一套25配置的敏感性测试验证了工作流程，揭示了不同种子和修订机制下的变化，并说明了事后污染如何改变测量的性能。三十一项自动化测试检查了时间与集成契约。真实的ALFRED和Chronos-2实验尚未执行；不声称任何经验性的基础模型优势。

英文摘要

Historical observations are not always fixed: statistical agencies revise previously published values as new evidence arrives. Forecasting from a contemporary download can therefore expose a model to information unavailable at the date it purportedly made a prediction. We propose VINTAGE-TS, a revision-aware adaptation of a time-series foundation model that distinguishes observation time from information-availability time. Its targets are the next period's first-published value and the value available a fixed number of days after that publication; neither is declared final truth. A joint predictive distribution preserves dependence between these targets and exposes uncertainty about their difference. We specify an ALFRED-based rolling evaluation, a matched Chronos-2 comparison, conventional and revision-aware baselines, and a separate audit of pretraining overlap. The accompanying software implements validity-interval reconstruction, delayed-label filtering, a frozen-backbone adapter interface, and reproducible diagnostics. An executed synthetic demonstration and a 25-configuration sensitivity suite verify the workflow, expose variation across seeds and revision regimes, and illustrate how hindsight contamination changes measured performance. Thirty one automated tests check temporal and integration contracts. Real ALFRED and Chronos-2 experiments have not been executed; no empirical foundation-model advantage is claimed.

发表机构

• Superior University Lahore（拉合尔卓越大学）

机构由 AI 辅助整理，请以论文原文为准。