{"type":"ScholarlyArticle","title":"Towards Cross-Modality Modeling for Time Series Analytics: A Survey in the LLM Era","authors":[{"name":"Chenxi Liu","slug":"chenxi-liu","given":"Chenxi","family":"Liu","affiliation":""},{"name":"Shaowen Zhou","slug":"shaowen-zhou","given":"Shaowen","family":"Zhou","affiliation":""},{"name":"Qianxiong Xu","slug":"qianxiong-xu","given":"Qianxiong","family":"Xu","affiliation":""},{"name":"Hao Miao","slug":"hao-miao","given":"Hao","family":"Miao","affiliation":""},{"name":"Cheng Long","slug":"cheng-long","given":"Cheng","family":"Long","affiliation":""},{"name":"Ziyue Li","slug":"ziyue-li","given":"Ziyue","family":"Li","affiliation":""},{"name":"Rui Zhao","slug":"rui-zhao","given":"Rui","family":"Zhao","affiliation":""}],"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.","doi":"10.24963/ijcai.2025/1173","url":"https://ijcai.org/proceedings/2025/1173","pdf":"https://ijcai.org/proceedings/2025/1173.pdf","markdown":"https://ijcai.org/proceedings/2025/1173.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1173","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Survey Track","track":"Survey Track","number":1173,"pages":{"first":10564,"last":10572},"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"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}