Pith. sign in

REVIEW 10 cited by

Large Language Models for Time Series: A Survey

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2402.01801 v3 pith:OI5MZABP submitted 2024-02-02 cs.LG cs.AIcs.CL

Xiyuan Zhang , Ranak Roy Chowdhury , Rajesh K. Gupta , Jingbo Shang This is my paper · ORCID
classification cs.LGcs.AIcs.CL
keywords llmsseriestimesurveyanalysisdatalanguagetext
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision. Going beyond text, image and graphics, LLMs present a significant potential for analysis of time series data, benefiting domains such as climate, IoT, healthcare, traffic, audio and finance. This survey paper provides an in-depth exploration and a detailed taxonomy of the various methodologies employed to harness the power of LLMs for time series analysis. We address the inherent challenge of bridging the gap between LLMs' original text data training and the numerical nature of time series data, and explore strategies for transferring and distilling knowledge from LLMs to numerical time series analysis. We detail various methodologies, including (1) direct prompting of LLMs, (2) time series quantization, (3) aligning techniques, (4) utilization of the vision modality as a bridging mechanism, and (5) the combination of LLMs with tools. Additionally, this survey offers a comprehensive overview of the existing multimodal time series and text datasets and delves into the challenges and future opportunities of this emerging field. We maintain an up-to-date Github repository which includes all the papers and datasets discussed in the survey.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Cost-Effective Multimodal LLM Reasoning Framework for Question Answering over Irregular Clinical Time Series

    cs.AI 2026-07 conditional novelty 6.0 of 10

    ClinPRISM reaches 49.83% average accuracy on CLIR-Bench irregular clinical time-series QA using a 4B LLM, 16 temporal tokens, and 0.15 s/question.

  2. CLIR-Bench: Benchmarking Multimodal Question Answering over Irregular Clinical Time Series

    cs.CL 2026-07 conditional novelty 6.0 of 10

    CLIR-Bench shows generalist and time-series LLMs struggle to ground clinical answers in sparse irregular ICU evidence, with top accuracy near 50% and weak causal evidence use.

  3. TSAQA: Time Series Analysis Question And Answering Benchmark

    cs.AI 2026-01 conditional novelty 6.0 of 10

    TSAQA provides 210k QA samples across 13 domains and six tasks, showing current LLMs score at most 65.08% zero-shot and struggle most with temporal-order reasoning.

  4. Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs

    cs.LG 2025-06 unverdicted novelty 6.0 of 10

    Time-R1 trains LLMs via supervised fine-tuning followed by reinforcement learning with a time-series-specific reward and non-uniform GRIP sampling to enable multi-step reasoning that improves forecasting accuracy.

  5. ELATE: Evolutionary Language model for Automated Time-series Engineering

    cs.LG 2025-08 conditional novelty 5.0 of 10

    An LLM-guided evolutionary feature engineering method for time-series forecasting reduces RMSE by 8.4% on average across seven datasets.

  6. Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification

    cs.LG 2025-06 conditional novelty 5.0 of 10

    A systematic review of GAN-based longitudinal data imputation that categorizes methods and shows that most ignore missingness mechanisms, static features, and mixed data types.

  7. Graph-Based Physics-Guided Urban PM2.5 Air Quality Imputation with Constrained Monitoring Data

    cs.LG 2025-06 conditional novelty 5.0 of 10

    GraPhy, a physics-inspired graph neural network with wind-based edge features and learnable diffusion scaling, reports the best PM2.5 imputation accuracy among six baselines on 41 sensors in Fresno, California.

  8. Technology-assisted Personalized Yoga for Better Health -- Challenges and Outlook

    cs.HC 2025-08 unverdicted novelty 4.0 of 10

    A vision statement for personalized yoga decision support whose claimed content cannot be verified, because the supplied full text is a different paper on maritime drift prediction.

  9. FAF: A Feature-Adaptive Framework for Few-Shot Time Series Forecasting

    cs.LG 2025-06 reject novelty 4.0 of 10

    A feature-adaptive meta-learning framework for few-shot time series forecasting reports large gains, but its evaluation uses one to nine test tasks per dataset, lacks error bars, and contains numerical and preprocessi...

  10. Large Language models for Time Series Analysis: Techniques, Applications, and Challenges

    cs.LG 2025-05 reject novelty 3.0 of 10

    A review of LLM-based time series analysis that proposes several taxonomies, but is undermined by citation errors and a lack of systematic methodology.

Pith tools