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

Paper · 2024 · Ming Jin, Yifan Zhang, Wei Chen et al.

Summary

This ICML 2024 position paper argues that time series research has fallen behind the push toward general-purpose AI. Most current time series models are built for a single task, depend on domain expertise and heavy tuning, and focus mainly on prediction. The authors propose that LLMs could act as a central hub for time series analysis. They would combine textual instructions and domain context with numeric sequences, use their own knowledge and reasoning, and rely on pre-trained time series models for basic pattern recognition. The paper does not introduce new datasets or experiments. Instead, it reviews existing work, groups it into three roles for LLMs (enhancer, predictor, agent) and places it on a four-generation roadmap of time series models. It also lists open problems, including hallucination, alignment with human preferences, concept drift, multi-agent setups and computational cost. The paper matters as an organizing map for a fast-growing interdisciplinary area, including emerging tasks such as modality switching and time series question answering.

Key points

  • Three integration roles: LLM-assisted Enhancer (enriches data and models), LLM-centered Predictor (performs forecasting and other tasks directly) and LLM-empowered Agent (drives the analysis itself).
  • Four-generation roadmap:
    • statistical models (ARIMA, Holt-Winters; roughly 1950s–2000s);
    • neural models (RNNs, TCNs, STGNNs; 2010s);
    • pre-trained models (TF-C, TimeCLR);
    • LLM-centric models (Time-LLM, SocioDojo).
  • Data framing: the paper distinguishes univariate series, multivariate series and spatial time series, where the last is modeled as a sequence of graph snapshots.
  • Relevant LLM abilities: the authors highlight in-context learning, instruction following and chain-of-thought reasoning. Example models cited are GPT-4, PaLM and Llama.
  • Future directions: trustworthy and aligned agents, injection of domain knowledge, lifelong learning under concept drift, multi-agent time series systems, and more efficient handling of tokenized numerical data.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Zhejiang UniversityHangzhou · China236 works · 14,072 citations🏛️Chinese Academy of SciencesBeijing · China256 works · 25,136 citations🎓East China Normal UniversityShanghai · China73 works · 2,653 citations🎓The Hong Kong University of Science and Technology (Guangzhou)Guangzhou · China73 works · 1,936 citations🎓Griffith UniversityBrisbane · Australia12 works · 4,254 citations

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 19,503 worksArtificial Intelligence · 18,805 worksArts and Humanities · 17,959 worksLanguage and Linguistics · 17,789 worksDecision Sciences · 306 worksSignal Processing · 499 worksManagement Science and Operations Research · 145 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 4,145 works

Cite

Cite as Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan and Qingsong Wen. 2024. Position: What Can Large Language Models Tell Us about Time Series Analysis. arXiv:2402.02713.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2402.02713 · arXiv 2402.02713

@misc{jin2024position,
  title = {{Position: What Can Large Language Models Tell Us about Time Series Analysis}},
  author = {Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan and Qingsong Wen},
  year = {2024},
  doi = {10.48550/arxiv.2402.02713},
  eprint = {2402.02713},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2402.02713}
}

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