mlsanityData to 14 Oct 2026

Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotation

arXiv 2410.17462 ↗First posted 22 Oct 2024cs.AI cs.CLcited by 21

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Abstract (arXiv)

Time series data is ubiquitous across various domains, including manufacturing, finance, and healthcare. High-quality annotations are essential for effectively understanding time series and facilitating downstream tasks; however, obtaining such annotations is challenging, particularly in mission-critical domains. In this paper, we propose TESSA, a multi-agent system designed to automatically generate both general and domain-specific annotations for time series data. TESSA introduces two agents: a general annotation agent and a domain-specific annotation agent. The general agent captures common patterns and knowledge across multiple source domains, leveraging both time-series-wise and text-wise features to generate general annotations. Meanwhile, the domain-specific agent utilizes limited annotations from the target domain to learn domain-specific terminology and generate targeted annotations. Extensive experiments on multiple synthetic and real-world datasets demonstrate that TESSA effectively generates high-quality annotations, outperforming existing methods.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 21 of its 37 references that are in the map. First posted on arXiv 22 Oct 2024.

Builds on

21 of 37 references.

References · 21

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
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  5. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
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  12. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 500
9 more
  1. 2021VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Expertscited by 425
  2. 2023TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Seriescited by 169
  3. 2023LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasterscited by 129
  4. 2021Voice2Series: Reprogramming Acoustic Models for Time Series Classificationcited by 91
  5. 2024A Survey of Large Language Models in Finance (FinLLMs)cited by 79
  6. 2021PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domainscited by 61
  7. 2024Empowering Time Series Analysis with Large Language Models: A Surveycited by 60
  8. 2024LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learningcited by 17
  9. 2023UDApter -- Efficient Domain Adaptation Using Adapterscited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 14; 2026: 5.

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  1. 2025Multi-modal Time Series Analysis: A Tutorial and Surveycited by 24
  2. 2025A Comprehensive Survey on Reinforcement Learning-based Agentic Search: Foundations, Roles, Optimizations, Evaluations, and Applicationscited by 22
  3. 2025A Survey of Reasoning and Agentic Systems in Time Series with Large Language Modelscited by 21
  4. 2026Position: Agentic Evolution is the Path to Evolving LLMscited by 16
  5. 2024Multi-Pair Temporal Sentence Grounding via Multi-Thread Knowledge Transfer Networkcited by 15
  6. 2025Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Modelscited by 15
  7. 2025LanP: Rethinking the Impact of Language Priors in Large Vision-Language Modelscited by 14
  8. 2025Evaluating System 1 vs. 2 Reasoning Approaches for Zero-Shot Time Series Forecasting: A Benchmark and Insightscited by 12
  9. 2026MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolutioncited by 12
  10. 2026Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMscited by 9
  11. 2025DualSG: A Dual-Stream Explicit Semantic-Guided Multivariate Time Series Forecasting Frameworkcited by 7
  12. 2025CrowdAgent: Multi-Agent Managed Multi-Source Annotation Systemcited by 5
9 more
  1. 2025Reasoning on Time-Series for Financial Technical Analysiscited by 5
  2. 2025How Far are LLMs from Real Search? A Comprehensive Study on Efficiency, Completeness, and Inherent Capabilitiescited by 4
  3. 2025AXIS: Explainable Time Series Anomaly Detection with Large Language Modelscited by 4
  4. 2024Stealing Training Graphs from Graph Neural Networkscited by 3
  5. 2025Image Corruption-Inspired Membership Inference Attacks against Large Vision-Language Modelscited by 3
  6. 2025Towards Interpretable and Trustworthy Time Series Reasoning: A BlueSky Visioncited by 2
  7. 2026LLM Agents for Time-Series: A Surveycited by 1
  8. 2025xTime: Extreme Event Prediction with Hierarchical Knowledge Distillation and Expert Fusioncited by 0
  9. 2026LineupRL: Verifiable Reinforcement Learning for Time Series Captioning via Caption-to-Series Identificationcited by 0