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

• URL: http://arxiv.org/abs/2505.02583v1
• Date: Mon, 05 May 2025 11:35:33 GMT
• 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,
• Abstract summary: Large Language Models (LLMs) have emerged as a new paradigm for time series analytics.<n>LLMs are pre-trained on textual corpora and are not inherently optimized for time series.<n>This survey is designed for a range of professionals, researchers, and practitioners interested in LLM-based time series modeling.
• Score: 24.980206999214552
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proliferation of edge devices has generated an unprecedented volume of time series data across different domains, motivating various 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.

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