---
id: 20260617-T0-10
title: "不规则时间序列问答新方法：通过工具推理实现可验证智能体数据分析"
title_en: "New Method Solves Irregular Time Series QA via Tool-Grounded Reasoning"
url: https://ai.daily.yangsir.net/daily/20260617-T0-10
issue_date: 2026-06-17
publish_date: 2026-06-16T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2606.15107
---

# 不规则时间序列问答新方法：通过工具推理实现可验证智能体数据分析

最新研究提出解决不规则时间序列问答（TSQA）的新方法，针对真实世界中异步观测、缺失值非随机等问题，通过工具推理实现可验证的智能体数据分析。该研究指出，现有时间序列模型普遍假设数据规则，但实际场景中传感器采样频率差异大、观测时间不固定，导致传统方法效果有限。新方案通过工具推理增强模型对复杂时序数据的处理能力，为金融、物联网等领域的 irregular 数据分析提供新思路。开发者可直接应用该方法改进时间序列预测系统的鲁棒性。

## English Version

**New Method Solves Irregular Time Series QA via Tool-Grounded Reasoning**

Researchers introduce a new method to solve irregular time series QA (TSQA), addressing real-world challenges like asynchronous observations and non-random missing values. Unlike existing models assuming regular data, this approach uses tool-grounded reasoning to handle variable sampling frequencies across sensors and operational windows. The solution enhances robustness for time series prediction systems, directly applicable to finance and IoT sectors where irregular data is common.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2606.15107)

**详情页**：https://ai.daily.yangsir.net/daily/20260617-T0-10

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