让大模型理解时间序列数据，打通文本与数值信号的分析壁垒。
LLMs Meet Cross-Modal Time Series Analytics: Overview and Directions
- 通过转换、对齐、融合三种策略实现文本与时间序列跨模态建模
- 现有方法可应用于异常检测、预测等多类下游任务
- 适合关注大模型在工业时序分析中应用的研究者
大语言模型（LLMs）因其庞大的参数量和与时间序列相似的序列特性，成为时间序列分析的新兴范式。然而，由于LLMs在纯文本语料上预训练，缺乏对时间序列数据的原生优化，存在跨模态鸿沟。本文提供了一个最新的综述，系统梳理基于LLM的跨模态时间序列分析方法。提出一个分类框架，将现有方法按跨模态建模策略分为三类：转换、对齐与融合，并讨论其在异常检测、趋势预测等下游任务中的应用。同时总结了当前面临的开放挑战。本教程旨在推动LLMs在真实世界跨模态时间序列分析中的实用落地，兼顾效果与效率。参与者将全面了解当前进展、方法论及未来研究方向。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have emerged as a promising paradigm for time series analytics, leveraging their massive parameters and the shared sequential nature of textual and time series data. However, a cross-modality gap exists between time series and textual data, as LLMs are pre-trained on textual corpora and are not inherently optimized for time series. In this tutorial, we provide an up-to-date overview of LLM-based cross-modal time series analytics. We introduce a taxonomy that classifies existing approaches into three groups based on cross-modal modeling strategies, e.g., conversion, alignment, and fusion, and then discuss their applications across a range of downstream tasks. In addition, we summarize several open challenges. This tutorial aims to expand the practical application of LLMs in solving real-world problems in cross-modal time series analytics while balancing effectiveness and efficiency. Participants will gain a thorough understanding of current advancements, methodologies, and future research directions in cross-modal time series analytics.
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