mlsanityData to 7 Oct 2026

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

arXiv 2402.02713 ↗First posted 5 Feb 2024cs.LG cs.AIcited by 47

Not on the map. Why?

Abstract (arXiv)

Time series analysis is essential for comprehending the complexities inherent in various realworld 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.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). Builds on 79 of its 108 references that are in the map. First posted on arXiv 5 Feb 2024.

Builds on

79 of 108 references.

References · 79

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  6. 2023Visual Instruction Tuningcited by 7,792
  7. 2020Scaling Laws for Neural Language Modelscited by 6,411
  8. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  9. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  10. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  11. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  12. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
38 more
  1. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 2,950
  2. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  3. 2023A Survey of Large Language Modelscited by 2,537
  4. 2022Emergent Abilities of Large Language Modelscited by 2,483
  5. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  6. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  7. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,462
  8. 2022Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agentscited by 1,029
  9. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 873
  10. 2023Vision-Language Models for Vision Tasks: A Surveycited by 834
  11. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
  12. 2023Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Modelscited by 659
  13. 2023A decoder-only foundation model for time-series forecastingcited by 650
  14. 2023Instruction Tuning for Large Language Models: A Surveycited by 617
  15. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
  16. 2019CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Datacited by 544
  17. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 491
  18. 2022Transformers in Time Series: A Surveycited by 489
  19. 2023Aligning Text-to-Image Models using Human Feedbackcited by 418
  20. 2023A Survey of Hallucination in Large Foundation Modelscited by 365
  21. 2023Analyzing and Mitigating Object Hallucination in Large Vision-Language Modelscited by 314
  22. 2023FreshLLMs: Refreshing Large Language Models with Search Engine Augmentationcited by 308
  23. 2021An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQAcited by 298
  24. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 271
  25. 2023The False Promise of Imitating Proprietary LLMscited by 254
  26. 2022Zero-Shot Video Question Answering via Frozen Bidirectional Language Modelscited by 229
  27. 2020Time Series Data Augmentation for Deep Learning: A Surveycited by 225
  28. 2023Fine-tuning Language Models for Factualitycited by 219
  29. 2023PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Financecited by 217
  30. 2023OpenAGI: When LLM Meets Domain Expertscited by 196
  31. 2023Large Language Models as General Pattern Machinescited by 196
  32. 2022PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecastingcited by 170
  33. 2023LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmarkcited by 169
  34. 2023AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Modelscited by 169
  35. 2023TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Seriescited by 165
  36. 2023TimeGPT-1cited by 149
  37. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  38. 2023UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecastingcited by 139

50 most cited of 79 All 79 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 9; 2025: 26; 2026: 12.

47 papers.

Sort:

By area:

  1. 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Expertscited by 280
  2. 2024CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patternscited by 102
  3. 2024Can LLMs Understand Time Series Anomalies?cited by 41
  4. 2025ITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Datasetcited by 35
  5. 2025TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Modelscited by 24
  6. 2024Empowering Time Series Analysis with Foundation Models: A Comprehensive Surveycited by 23
  7. 2025A Survey of Reasoning and Agentic Systems in Time Series with Large Language Modelscited by 21
  8. 2024Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Taskscited by 15
  9. 2025Out-of-Distribution Generalization in Time Series: A Surveycited by 15
  10. 2025Towards Cross-Modality Modeling for Time Series Analytics: A Survey in the LLM Eracited by 11
  11. 2024ST-LoRA: Low-rank Adaptation for Spatio-Temporal Forecastingcited by 9
  12. 2025Vision-Enhanced Time Series Forecasting via Latent Diffusion Modelscited by 9
35 more
  1. 2025There are no Champions in Supervised Long-Term Time Series Forecastingcited by 8
  2. 2025From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?cited by 7
  3. 2025Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectivescited by 7
  4. 2025ProMind-LLM: Proactive Mental Health Care via Causal Reasoning with Sensor Datacited by 6
  5. 2025mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scalecited by 6
  6. 2025CC-Time: Cross-Model and Cross-Modality Time Series Forecastingcited by 6
  7. 2025QuAnTS: Question Answering on Time Seriescited by 5
  8. 2024LLM-ABBA: Understanding time series via symbolic approximationcited by 4
  9. 2025Enhancing Time Series Forecasting via Multi-Level Text Alignment with LLMscited by 4
  10. 2025TsLLM: Augmenting LLMs for General Time Series Understanding and Predictioncited by 4
  11. 2024Transfer Learning with Foundational Models for Time Series Forecasting using Low-Rank Adaptationscited by 3
  12. 2025Towards Interpretable Time Series Foundation Modelscited by 3
  13. 2025How Effective are Large Time Series Models in Hydrology? A Study on Water Level Forecasting in Evergladescited by 2
  14. 2025Beyond Fixed Patches: Enhancing GPTs for Financial Prediction with Adaptive Segmentation and Learnable Waveletscited by 2
  15. 2025Large Language models for Time Series Analysis: Techniques, Applications, and Challengescited by 2
  16. 2025EventTSF: Event-Aware Non-Stationary Time Series Forecastingcited by 2
  17. 2026CPiRi: Channel Permutation-Invariant Relational Interaction for Multivariate Time Series Forecastingcited by 2
  18. 2026Seeking SOTA: Time-Series Forecasting Must Adopt Taxonomy-Specific Evaluation to Dispel Illusory Gainscited by 2
  19. 2025Rating Quality of Diverse Time Series Data by Meta-learning from LLM Judgmentcited by 1
  20. 2025RAST: A Retrieval Augmented Spatio-Temporal Framework for Traffic Predictioncited by 1
  21. 2026Multi-Modal Time Series Prediction via Mixture of Modulated Expertscited by 1
  22. 2026Position: Universal Time Series Foundation Models Rest on a Category Errorcited by 1
  23. 2026Semantics or Structure? Auditing Text Sensitivity in Multimodal Time-Series Forecastingcited by 1
  24. 2024Apollo-Forecast: Overcoming Aliasing and Inference Speed Challenges in Language Models for Time Series Forecastingcited by 0
  25. 2025NSW-EPNews: A News-Augmented Benchmark for Electricity Price Forecasting with LLMscited by 0
  26. 2025Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Representation Alignmentcited by 0
  27. 2025Select, then Balance: Exploring Exogenous Variable Modeling of Spatio-Temporal Forecastingcited by 0
  28. 2025TransactionGPTcited by 0
  29. 2026A Comparative Study of Adaptation Strategies for Time Series Foundation Models in Anomaly Detectioncited by 0
  30. 2026CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecastingcited by 0
  31. 2026Cross-Machine Anomaly Detection Leveraging Pre-trained Time-series Modelcited by 0
  32. 2026CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecastingcited by 0
  33. 2026InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMscited by 0
  34. 2026Can Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM-based Approachcited by 0
  35. 2026Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretabilitycited by 0