LLMs Meet Cross-Modal Time Series Analytics: Overview and Directions

• URL: http://arxiv.org/abs/2507.10620v1
• Date: Sun, 13 Jul 2025 23:47:32 GMT
• Title: LLMs Meet Cross-Modal Time Series Analytics: Overview and Directions
• Authors: Chenxi Liu, Hao Miao, Cheng Long, Yan Zhao, Ziyue Li, Panos Kalnis,
• Abstract summary: Large Language Models (LLMs) have emerged as a promising paradigm for time series analytics.<n>This tutorial aims to expand the practical application of LLMs in solving real-world problems in cross-modal time series analytics.
• Score: 25.234786025837423
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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