---
title: "Towards Cross-Modality Modeling for Time Series Analytics: A Survey in the LLM Era"
authors:
  - "Chenxi Liu"
  - "Shaowen Zhou"
  - "Qianxiong Xu"
  - "Hao Miao"
  - "Cheng Long"
  - "Ziyue Li"
  - "Rui Zhao"
doi: "10.24963/ijcai.2025/1173"
url: "https://ijcai.org/proceedings/2025/1173"
pdf: "https://ijcai.org/proceedings/2025/1173.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "10564-10572"
track: "Survey Track"
keywords:
  - "Machine Learning: ML: Multi-modal learning"
  - "Machine Learning: ML: Time series and data streams"
  - "Natural Language Processing: NLP: Language models"
  - "Data Mining: DM: Mining spatial and/or temporal data"
---

# Towards Cross-Modality Modeling for Time Series Analytics: A Survey in the LLM Era

Chenxi Liu, Shaowen Zhou, Qianxiong Xu, Hao Miao, Cheng Long, Ziyue Li, Rui Zhao

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 10564-10572. DOI: [10.24963/ijcai.2025/1173](https://doi.org/10.24963/ijcai.2025/1173)

## Abstract

The proliferation of edge devices has generated an unprecedented volume of time series data across different domains, motivating a variety of well-customized methods. Recently, Large Language Models (LLMs) have emerged as a new paradigm for time series analytics by leveraging the shared sequential nature of textual data and time series. However, a fundamental cross-modality gap between time series and LLMs exists, as LLMs are pre-trained on textual corpora and are not inherently optimized for time series. Many recent proposals are designed to address this issue. In this survey, we provide an up-to-date overview of LLMs-based cross-modality modeling for time series analytics. We first introduce a taxonomy that classifies existing approaches into four groups based on the type of textual data employed for time series modeling. We then summarize key cross-modality strategies, e.g., alignment and fusion, and discuss their applications across a range of downstream tasks. Furthermore, we conduct experiments on multimodal datasets from different application domains to investigate effective combinations of textual data and cross-modality strategies for enhancing time series analytics. Finally, we suggest several promising directions for future research. This survey is designed for a range of professionals, researchers, and practitioners interested in LLM-based time series modeling.