Related papers: Position: Empowering Time Series Reasoning with Multimodal LLMs

Position: Empowering Time Series Reasoning with Multimodal LLMs

URL: http://arxiv.org/abs/2502.01477v1
Date: Mon, 03 Feb 2025 16:10:48 GMT
Title: Position: Empowering Time Series Reasoning with Multimodal LLMs
Authors: Yaxuan Kong, Yiyuan Yang, Shiyu Wang, Chenghao Liu, Yuxuan Liang, Ming Jin, Stefan Zohren, Dan Pei, Yan Liu, Qingsong Wen,
Abstract summary: We argue that multimodal language models (MLLMs) can enable more powerful and flexible reasoning for time series analysis. We call on researchers and practitioners to leverage this potential by developing strategies that prioritize trust, interpretability, and robust reasoning in MLLMs.
Score: 49.73647759532127
License:
Abstract: Understanding time series data is crucial for multiple real-world applications. While large language models (LLMs) show promise in time series tasks, current approaches often rely on numerical data alone, overlooking the multimodal nature of time-dependent information, such as textual descriptions, visual data, and audio signals. Moreover, these methods underutilize LLMs' reasoning capabilities, limiting the analysis to surface-level interpretations instead of deeper temporal and multimodal reasoning. In this position paper, we argue that multimodal LLMs (MLLMs) can enable more powerful and flexible reasoning for time series analysis, enhancing decision-making and real-world applications. We call on researchers and practitioners to leverage this potential by developing strategies that prioritize trust, interpretability, and robust reasoning in MLLMs. Lastly, we highlight key research directions, including novel reasoning paradigms, architectural innovations, and domain-specific applications, to advance time series reasoning with MLLMs.

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