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single-agent time series systems

Single-agent time series systems are artificial intelligence frameworks in which an individual autonomous agent, typically powered by a large language model or foundation model, manages and executes end-to-end workflows for analyzing sequential temporal data. Unlike traditional static models that perform isolated operations such as basic forecasting or classification, a single-agent system functions as a centralized decision-maker capable of reasoning, planning, maintaining memory, and invoking external computational tools or specialized algorithms across multi-step processes. Through this unified architecture, the agent can interpret complex temporal patterns, adapt its analytical pipeline based on intermediate feedback, and facilitate natural language interactions, question answering, and multimodal data transitions for time series tasks.

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Position: What Can Large Language Models Tell Us about Time Series Analysis

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

OrganizationsChinese Academy of SciencesEast China Normal UniversityGriffith UniversityMicrosoftSquirrel AIThe Hong Kong University of Science and TechnologyZhejiang University

Why you should read this

Categorizes the emerging roles of large language models in time series analysis as data enhancers, predictors, and autonomous agents while identifying concrete integration strategies and open research opportunities for building universal time series intelligence.

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.

Added

2026-09-26