Position: What Can Large Language Models Tell Us about Time Series Analysis
Ming Jin Yifan Zhang Wei Chen Kexin Zhang Yuxuan Liang Bin Yang Jindong Wang Shirui Pan Qingsong Wen
Abstract
Time series analysis is essential for comprehending the complexities inherent in various real-world systems and applications. Although large language models (LLMs) have recently made significant strides, the development of artificial general intelligence (AGI) equipped with time series analysis capabilities remains in its nascent phase. Most existing time series models heavily rely on domain knowledge and extensive model tuning, predominantly focusing on prediction tasks. In this paper, we argue that current LLMs have the potential to revolutionize time series analysis, thereby promoting efficient decision-making and advancing towards a more universal form of time series analytical intelligence. Such advancement could unlock a wide range of possibilities, including time series modality switching and question answering. We encourage researchers and practitioners to recognize the potential of LLMs in advancing time series analysis and emphasize the need for trust in these related efforts. Furthermore, we detail the seamless integration of time series analysis with existing LLM technologies and outline promising avenues for future research.
中文速览
时序数据（time series）广泛存在于金融、交通、医疗等领域，但现有分析模型大多依赖大量领域知识和繁琐调参，只能处理预测这类单一任务，距离真正的通用智能还差得很远。这篇论文提出一个核心观点：大语言模型（Large Language Models, LLMs）有潜力彻底改变时序分析，可以从三个层面发挥作用——作为数据和模型的增强器、作为更强的预测器、以及作为能主动规划决策的下一代智能体。作者系统梳理了现有将LLM与时序分析结合的工作，涵盖数据增强、模型融合、问答交互、模态转换等多种形式，并指出了零样本泛化、可解释推理、跨模态对齐等未来值得深耕的方向。这项工作的价值在于：它为时序分析从"专用预测工具"迈向"通用智能系统"描绘了清晰路线图，有助于推动两个领域的研究者形成合力，加速实现能跨任务、跨领域统一处理时序数据的人工通用智能。
原文 arXiv:2402.02713;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2402.02713v2