mlsanityData to 7 Oct 2026

Agentic Retrieval-Augmented Generation for Time Series Analysis

arXiv 2408.14484 ↗First posted 18 Aug 2024cs.AI cs.CL cs.LGcited by 13

On the map: Retrieval-augmented generation → Agentic RAG, agents and memory

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Abstract (arXiv)

Time series modeling is crucial for many applications, however, it faces challenges such as complex spatio-temporal dependencies and distribution shifts in learning from historical context to predict task-specific outcomes. To address these challenges, we propose a novel approach using an agentic Retrieval-Augmented Generation (RAG) framework for time series analysis. The framework leverages a hierarchical, multi-agent architecture where the master agent orchestrates specialized sub-agents and delegates the end-user request to the relevant sub-agent. The sub-agents utilize smaller, pre-trained language models (SLMs) customized for specific time series tasks through fine-tuning using instruction tuning and direct preference optimization, and retrieve relevant prompts from a shared repository of prompt pools containing distilled knowledge about historical patterns and trends to improve predictions on new data. Our proposed modular, multi-agent RAG approach offers flexibility and achieves state-of-the-art performance across major time series tasks by tackling complex challenges more effectively than task-specific customized methods across benchmark datasets.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation. Builds on 30 of its 48 references that are in the map. First posted on arXiv 18 Aug 2024.

Builds on

30 of 48 references.

References · 30

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
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  8. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  9. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  10. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  11. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 786
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18 more
  1. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 744
  2. 2024Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Surveycited by 641
  3. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
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  5. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 387
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  8. 2022Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structurescited by 158
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  11. 2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspectivecited by 126
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 8; 2026: 4.

13 papers.

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  1. 2024Retrieval Augmented Time Series Forecastingcited by 40
  2. 2025MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoningcited by 24
  3. 2025A Survey of Reasoning and Agentic Systems in Time Series with Large Language Modelscited by 21
  4. 2025Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challengescited by 19
  5. 2026Self-Improvements in Modern Agentic Systems: A Surveycited by 17
  6. 2025InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generationcited by 15
  7. 2025Patho-AgenticRAG: Towards Multimodal Agentic Retrieval-Augmented Generation for Pathology VLMs via Reinforcement Learningcited by 13
  8. 2026GraphSkill: Documentation-Guided Hierarchical Retrieval-Augmented Coding for Complex Graph Reasoningcited by 4
  9. 2025Confident RAG: Enhancing the Performance of LLMs for Mathematics Question Answering through Multi-Embedding and Confidence Scoringcited by 2
  10. 2025Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generationcited by 2
  11. 2026RAGExplorer: A Visual Analytics System for the Comparative Diagnosis of RAG Systemscited by 2
  12. 2025Agent-Enhanced Large Language Models for Researching Political Institutionscited by 1
1 more
  1. 2026Visual Reasoning over Time Series via Multi-Agent Systemcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
  2. 2026TS-RAG: Retrieval Augmented Generation for Time Series Forecasting
  3. 2025TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gathering
  4. 2024A Collaborative Multi-Agent Approach to Retrieval-Augmented Generation Across Diverse Data
  5. 2026A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces
  6. 2026Is Agentic RAG worth it? An experimental comparison of RAG approaches
  7. 2026Retrieval-Augmented Generation with Covariate Time Series
  8. 2024MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation