LLMs Meet Cross-Modal Time Series Analytics: Overview and Directions
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 25 of its 72 references that are in the map. First posted on arXiv 13 Jul 2025.
Builds on
25 of 72 references.
References · 25
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- 2024C2F-TP: A Coarse-to-Fine Denoising Framework for Uncertainty-Aware Trajectory Predictioncited by 9
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- 2024Unsupervised Time Series Anomaly Prediction with Importance-based Generative Contrastive Learningcited by 3
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