---
id: 20260927-T0-05
title: "时间序列基础模型多步预测不确定性大，SGA方法尝试量化"
title_en: "SGA Method Quantifies Uncertainty in Multi-Step Time-Series Foundation Model Forecasting"
url: https://ai.daily.yangsir.net/daily/20260927-T0-05
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/2609.28582
---

# 时间序列基础模型多步预测不确定性大，SGA方法尝试量化

arXiv新论文针对时间序列基础模型（TSFM）提出SGA方法，用于多步预测中的不确定性量化。研究指出，现有TSFM虽在长程预测上表现提升，但普遍存在显著固有不确定性。SGA通过建模预测分布，帮助用户在依赖TSFM做长期决策时评估风险，适用于能源、金融等场景。

## English Version

**SGA Method Quantifies Uncertainty in Multi-Step Time-Series Foundation Model Forecasting**

A new arXiv paper introduces SGA, a method for uncertainty quantification in multi-step forecasting with time-series foundation models (TSFMs). It notes that while TSFMs have advanced long-horizon prediction, they suffer significant inherent uncertainty. SGA models predictive distributions to help users assess risk when relying on TSFMs for long-term decisions in energy, finance, and beyond.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2609.28582)

**详情页**：https://ai.daily.yangsir.net/daily/20260927-T0-05

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