---
id: 20260927-T0-02
title: "时间序列基础模型新挑战：统计数据会被修订，模型如何不\"偷看未来\""
title_en: "Time-Series Foundation Models Face Data Revisions That Leak Future Information"
url: https://ai.daily.yangsir.net/daily/20260927-T0-02
issue_date: 2026-09-27
publish_date: 2026-09-26T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2609.28576
---

# 时间序列基础模型新挑战：统计数据会被修订，模型如何不"偷看未来"

arXiv新论文指出，历史观测数据并非固定不变——统计机构会随新证据修订已发布数值。若模型基于当前下载的数据训练，可能接触到其宣称预测时点尚不可得的信息，造成数据泄漏。研究聚焦时间序列基础模型如何理解并处理数据修订问题，对经济、气象等依赖修订数据的预测场景有直接影响。

## English Version

**Time-Series Foundation Models Face Data Revisions That Leak Future Information**

A new arXiv paper highlights that historical observations are not fixed—statistical agencies revise published values as new evidence arrives. Models forecasting from a contemporary data download may access information unavailable at the purported prediction date, causing leakage. The work examines how time-series foundation models should understand and handle data revisions, with direct implications for economics, weather, and other revision-heavy forecasting domains.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2609.28576)

**详情页**：https://ai.daily.yangsir.net/daily/20260927-T0-02

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