Beyond Numbers: A Survey of Time Series Analysis in the Era of Multimodal LLMs
Xiongxiao Xu, Yue Zhao, Philip S. Yu, Kai Shu · 2025
The rapid advancements in Multimodal Large Language Models (MLLMs) have garnered significant research attention and revolutionized various domains, including time series analysis. Notably, time series data can be represented in diverse modalities, making it highly compatible with the progress of MLLMs. This survey provides a comprehensive overview of time series analysis in the era of multimodal LLMs. We systematically summarize existing work from two perspectives: data (taxonomy of time series modalities) and models (taxonomy of multimodal LLMs). From a data perspective, we emphasize that time series, traditionally represented as a sequence of numbers with temporal order, can also be expressed in modalities such as text, images, graphs, audios, and tables. From a model perspective, we explore MLLMs that are either applicable or hold potential for specific time series modalities. Finally, we identify future research directions and key challenges at the intersection of time series and MLLMs, including the video modality, reasoning, agents, interpretability, and hallucinations. We curate and maintain a GitHub repository to facilitate the latest developments in this rapidly evolving field at https://github.com/mllm-ts/Awesome-Multimodal-LLMs-Time-Series .