mlsanityData to 7 Oct 2026

Foundation Models for Time Series Analysis: A Tutorial and Survey

arXiv 2403.14735 ↗First posted 21 Mar 2024cs.LGcited by 234

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Abstract (arXiv)

Time series analysis stands as a focal point within the data mining community, serving as a cornerstone for extracting valuable insights crucial to a myriad of real-world applications. Recent advances in Foundation Models (FMs) have fundamentally reshaped the paradigm of model design for time series analysis, boosting various downstream tasks in practice. These innovative approaches often leverage pre-trained or fine-tuned FMs to harness generalized knowledge tailored for time series analysis. This survey aims to furnish a comprehensive and up-to-date overview of FMs for time series analysis. While prior surveys have predominantly focused on either application or pipeline aspects of FMs in time series analysis, they have often lacked an in-depth understanding of the underlying mechanisms that elucidate why and how FMs benefit time series analysis. To address this gap, our survey adopts a methodology-centric classification, delineating various pivotal elements of time-series FMs, including model architectures, pre-training techniques, adaptation methods, and data modalities. Overall, this survey serves to consolidate the latest advancements in FMs pertinent to time series analysis, accentuating their theoretical underpinnings, recent strides in development, and avenues for future exploration.

Key facts. Cited by 234 papers in the map (data through 7 Oct 2026). Builds on 67 of its 127 references that are in the map. First posted on arXiv 21 Mar 2024.

Builds on

67 of 127 references.

References · 67

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38 more
  1. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
  2. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 588
  3. 2024Large Language Models: A Surveycited by 506
  4. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 491
  5. 2022Transformers in Time Series: A Surveycited by 489
  6. 2021TS2Vec: Towards Universal Representation of Time Seriescited by 463
  7. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 387
  8. 2022Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistencycited by 246
  9. 2024TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variablescited by 241
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  13. 2023TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Seriescited by 165
  14. 2023UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecastingcited by 139
  15. 2021AdaRNN: Adaptive Learning and Forecasting of Time Seriescited by 120
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  18. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 109
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 45; 2025: 104; 2026: 85.

234 papers.

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  1. 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Expertscited by 280
  2. 2025Sundial: A Family of Highly Capable Time Series Foundation Modelscited by 168
  3. 2024TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignmentcited by 97
  4. 2024Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Expertscited by 88
  5. 2024A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challengescited by 86
  6. 2024A Survey on Diffusion Models for Time Series and Spatio-Temporal Datacited by 66
  7. 2025Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecastingcited by 59
  8. 2024TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecastingcited by 55
  9. 2024Retrieval Augmented Time Series Forecastingcited by 40
  10. 2025TimeXL: Explainable Multi-modal Time Series Prediction with LLM-in-the-Loopcited by 39
  11. 2025TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecastercited by 36
  12. 2025Representation Learning for Tabular Data: A Comprehensive Surveycited by 36
38 more
  1. 2024TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Modelcited by 33
  2. 2025PDE-Transformer: Efficient and Versatile Transformers for Physics Simulationscited by 29
  3. 2024Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecastingcited by 26
  4. 2025Harnessing Vision Models for Time Series Analysis: A Surveycited by 26
  5. 2024Zero-shot forecasting of chaotic systemscited by 25
  6. 2024Fine-Tuning is Fine, if Calibratedcited by 24
  7. 2024Empowering Time Series Analysis with Foundation Models: A Comprehensive Surveycited by 23
  8. 2025Foundation Models for Spatio-Temporal Data Science: A Tutorial and Surveycited by 23
  9. 2025MIRA: Medical Time Series Foundation Model for Real-World Health Datacited by 21
  10. 2025A Survey of Reasoning and Agentic Systems in Time Series with Large Language Modelscited by 21
  11. 2025Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge Distillationcited by 20
  12. 2025Foundation Models for Time Series: A Surveycited by 19
  13. 2025SETransformer: A Hybrid Attention-Based Architecture for Robust Human Activity Recognitioncited by 19
  14. 2025HiMAE: Hierarchical Masked Autoencoders Discover Resolution-Specific Structure in Wearable Time Seriescited by 19
  15. 2025BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modelingcited by 18
  16. 2024Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognitioncited by 16
  17. 2024Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Taskscited by 15
  18. 2025Lightweight Online Adaption for Time Series Foundation Model Forecastscited by 14
  19. 2025Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learningcited by 14
  20. 2025DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignmentcited by 13
  21. 2024Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classificationcited by 12
  22. 2024Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecastingcited by 11
  23. 2025Relational Conformal Prediction for Correlated Time Seriescited by 11
  24. 2026Reverso: Efficient Time Series Foundation Models for Zero-shot Forecastingcited by 11
  25. 2024Beyond LoRA: Exploring Efficient Fine-Tuning Techniques for Time Series Foundational Modelscited by 10
  26. 2025Performance of Zero-Shot Time Series Foundation Models on Cloud Datacited by 10
  27. 2025Time Series Analysis in Frequency Domain: A Survey of Open Challenges, Opportunities and Benchmarkscited by 10
  28. 2025Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Reviewcited by 10
  29. 2025Rethinking Evaluation in the Era of Time Series Foundation Models: (Un)known Information Leakage Challengescited by 10
  30. 2024Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecastingcited by 9
  31. 2024A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classificationcited by 9
  32. 2025Generalizable deep learning for photoplethysmography-based blood pressure estimation -- A Benchmarking Studycited by 9
  33. 2024Exploring Kolmogorov-Arnold Networks for Interpretable Time Series Classificationcited by 8
  34. 2026From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detectioncited by 8
  35. 2024Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classificationcited by 7
  36. 2025Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedbackcited by 7
  37. 2025FlowState: Sampling-Rate-Equivariant Time-Series Forecastingcited by 7
  38. 2024Repurposing Foundation Model for Generalizable Medical Time Series Classificationcited by 6

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