mlsanityData to 14 Oct 2026

Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models

arXiv 2503.11411 ↗First posted 14 Mar 2025cs.LGcited by 15

On the map: Synthetic data and self-improvement for LLMs → Model collapse from synthetic data

Abstract (arXiv)

Time series analysis is crucial for understanding dynamics of complex systems. Recent advances in foundation models have led to task-agnostic Time Series Foundation Models (TSFMs) and Large Language Model-based Time Series Models (TSLLMs), enabling generalized learning and integrating contextual information. However, their success depends on large, diverse, and high-quality datasets, which are challenging to build due to regulatory, diversity, quality, and quantity constraints. Synthetic data emerge as a viable solution, addressing these challenges by offering scalable, unbiased, and high-quality alternatives. This survey provides a comprehensive review of synthetic data for TSFMs and TSLLMs, analyzing data generation strategies, their role in model pretraining, fine-tuning, and evaluation, and identifying future research directions.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Placed in Model collapse from synthetic data, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 51 of its 93 references that are in the map. First posted on arXiv 14 Mar 2025.

Builds on

51 of 93 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning4 Dec

Other references · 50

  1. 2025Qwen2.5-VL Technical Reportcited by 5,587
  2. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  3. 2023Video-LLaVA: Learning United Visual Representation by Alignment Before Projectioncited by 1,402
  4. 2024Chronos: Learning the Language of Time Seriescited by 759
  5. 2022TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Secondcited by 674
  6. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 632
  7. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 614
  8. 2018Deep learning for time series classification: a reviewcited by 576
  9. 2024DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understandingcited by 572
  10. 2022Transformers in Time Series: A Surveycited by 497
  11. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 482
  12. 2020Time Series Forecasting With Deep Learning: A Surveycited by 459
38 more
  1. 2024ShareGPT4Video: Improving Video Understanding and Generation with Better Captionscited by 388
  2. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 333
  3. 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Expertscited by 287
  4. 2023WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Researchcited by 267
  5. 2024On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Surveycited by 244
  6. 2024CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMscited by 234
  7. 2024Large Language Models for Data Annotation and Synthesis: A Surveycited by 205
  8. 2022Pretrained Language Models for Text Generation: A Surveycited by 122
  9. 2024Large Language Models for Time Series: A Surveycited by 114
  10. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 110
  11. 2024OmniBench: Towards The Future of Universal Omni-Language Modelscited by 102
  12. 2024ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Datacited by 100
  13. 2024Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Expertscited by 95
  14. 2024VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasterscited by 88
  15. 2024Context is Key: A Benchmark for Forecasting with Essential Textual Informationcited by 87
  16. 2025Step-Audio: Unified Understanding and Generation in Intelligent Speech Interactioncited by 83
  17. 2024Empowering Time Series Analysis with Large Language Models: A Surveycited by 60
  18. 2025From Tables to Time: Extending TabPFN-v2 to Time Series Forecastingcited by 60
  19. 2024TimeSeriesExam: A time series understanding examcited by 49
  20. 2025Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrativecited by 40
  21. 2024Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmarkcited by 38
  22. 2024Towards Time Series Reasoning with LLMscited by 37
  23. 2021When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?cited by 36
  24. 2023M3DBench: Let's Instruct Large Models with Multi-modal 3D Promptscited by 32
  25. 2025TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Datacited by 29
  26. 2023Large Pre-trained time series models for cross-domain Time series analysis taskscited by 23
  27. 2024Decoding Time Series with LLMs: A Multi-Agent Framework for Cross-Domain Annotationcited by 21
  28. 2024Exploring Representations and Interventions in Time Series Foundation Modelscited by 20
  29. 2023TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Seriescited by 18
  30. 2024Intervention-Aware Forecasting: Breaking Historical Limits from a System Perspectivecited by 17
  31. 2021Truth-Conditional Captioning of Time Series Datacited by 11
  32. 2025Time Series Language Model for Descriptive Caption Generationcited by 11
  33. 2024ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformercited by 10
  34. 2025TimeHF: Billion-Scale Time Series Models Guided by Human Feedbackcited by 10
  35. 2024Are Synthetic Time-series Data Really not as Good as Real Data?cited by 7
  36. 2024Towards Long-Context Time Series Foundation Modelscited by 5
  37. 2024Towards Foundation Time Series Model: To Synthesize Or Not To Synthesize?cited by 4
  38. 2024Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot Forecastingcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 9.

15 papers.

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  1. 2025A Survey of Reasoning and Agentic Systems in Time Series with Large Language Modelscited by 21
  2. 2025TempoPFN: Synthetic Pre-training of Linear RNNs for Zero-shot Time Series Forecastingcited by 17
  3. 2025CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Datacited by 13
  4. 2025VisionTS++: Cross-Modal Time Series Foundation Model with Continual Pre-trained Vision Backbonescited by 12
  5. 2026OATS: Online Data Augmentation for Time Series Foundation Modelscited by 3
  6. 2026TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuningcited by 3
  7. 2026Post-Training in Time Series Foundation Models: A Unifying Frameworkcited by 3
  8. 2025Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Eracited by 2
  9. 2025A More Realistic Evaluation of Cross-Frequency Transfer Learning and Foundation Forecasting Modelscited by 2
  10. 2026TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Modelscited by 2
  11. 2026Generating Expressive and Customizable Evals for Timeseries Data Analysis Agents with AgentFuelcited by 1
  12. 2026RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Modelscited by 1
3 more
  1. 2026Parallel Complex Diffusion for Scalable Time Series Generationcited by 0
  2. 2026Learning Spatio-Temporal Foundation Models from Pure Synthetic Datacited by 0
  3. 2026Foundations without Fundamentals: Zero-Shot Blind Spots in Time Series FMscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025The language of time: a language model perspective on time-series foundation models
  2. 2025Rethinking Evaluation in the Era of Time Series Foundation Models: (Un)known Information Leakage Challenges
  3. 2025Conversational Time Series Foundation Models: Towards Explainable and Effective Forecasting
  4. 2026TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale
  5. 2026Distillation of Synthetic Data for Time Series Foundation Models
  6. 2026Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters
  7. 2025Achieving Time Series Reasoning Requires Rethinking Model Design, Tasks Formulation, and Evaluation
  8. 2025Large Language models for Time Series Analysis: Techniques, Applications, and Challenges