# Empowering Time Series Analysis with Foundation Models: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2405.02358 · HTML: https://mlsanity.com/paper/2405.02358/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 77 of its 191 references that are in the map.
- First posted on arXiv 3 May 2024.

## Builds on (most cited first, 30 of 77)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://mlsanity.com/paper/2102.03334/) (2021)
- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://mlsanity.com/paper/2210.02186/) (2022)
- [Chronos: Learning the Language of Time Series](https://mlsanity.com/paper/2403.07815/) (2024)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [A decoder-only foundation model for time-series forecasting](https://mlsanity.com/paper/2310.10688/) (2023)
- [One Fits All:Power General Time Series Analysis by Pretrained LM](https://mlsanity.com/paper/2302.11939/) (2023)
- [Unified Training of Universal Time Series Forecasting Transformers](https://mlsanity.com/paper/2402.02592/) (2024)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Language Is Not All You Need: Aligning Perception with Language Models](https://mlsanity.com/paper/2302.14045/) (2023)
- [Large Language Models Are Zero-Shot Time Series Forecasters](https://mlsanity.com/paper/2310.07820/) (2023)

## Built on by (most cited first, 23 of 23)

- [Performance of Zero-Shot Time Series Foundation Models on Cloud Data](https://mlsanity.com/paper/2502.12944/) (2025)
- [Repurposing Foundation Model for Generalizable Medical Time Series Classification](https://mlsanity.com/paper/2410.03794/) (2024)
- [Generalized Prompt Tuning: Adapting Frozen Univariate Time Series Foundation Models for Multivariate Healthcare Time Series](https://mlsanity.com/paper/2411.12824/) (2024)
- [FTMixer: Frequency and Time Domain Representations Fusion for Time Series Modeling](https://mlsanity.com/paper/2405.15256/) (2024)
- [Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement](https://mlsanity.com/paper/2410.02147/) (2024)
- [Large Language models for Time Series Analysis: Techniques, Applications, and Challenges](https://mlsanity.com/paper/2506.11040/) (2025)
- [The language of time: a language model perspective on time-series foundation models](https://mlsanity.com/paper/2507.00078/) (2025)
- [ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection](https://mlsanity.com/paper/2606.01300/) (2026)
- [The Effect of Stochasticity in Score-Based Diffusion Sampling: a KL Divergence Analysis](https://mlsanity.com/paper/2506.11378/) (2025)
- [SignalMC-MED: A Multimodal Benchmark for Evaluating Biosignal Foundation Models on Single-Lead ECG and PPG](https://mlsanity.com/paper/2603.09940/) (2026)
- [Can LLM Coding Agents Reason About Time Series?](https://mlsanity.com/paper/2606.16545/) (2026)
- [Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models](https://mlsanity.com/paper/2608.08010/) (2026)
- [FlexTSF: A Flexible Forecasting Model for Time Series with Variable Regularities](https://mlsanity.com/paper/2410.23160/) (2024)
- [Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data](https://mlsanity.com/paper/2412.00403/) (2024)
- [Evaluating Time Series Foundation Models on Noisy Periodic Time Series](https://mlsanity.com/paper/2501.00889/) (2025)
- [LeForecast: Enterprise Hybrid Forecast by Time Series Intelligence](https://mlsanity.com/paper/2503.22747/) (2025)
- [Feature Fitted Online Conformal Prediction for Deep Time Series Forecasting Model](https://mlsanity.com/paper/2505.08158/) (2025)
- [Gradient Flow Matching for Learning Update Dynamics in Neural Network Training](https://mlsanity.com/paper/2505.20221/) (2025)
- [ss-Mamba: Semantic-Spline Selective State-Space Model](https://mlsanity.com/paper/2506.14802/) (2025)
- [Evaluation of Stress Detection as Time Series Events -- A Novel Window-Based F1-Metric](https://mlsanity.com/paper/2509.03240/) (2025)
- [ARTEMIS: A Neuro Symbolic Framework for Economically Constrained Market Dynamics](https://mlsanity.com/paper/2603.18107/) (2026)
- [Model-agnostic Retrieval-Augmented Extended Forecasting for time series](https://mlsanity.com/paper/2608.14054/) (2026)
- [Parameter-Efficient Adaptation of Pretrained Language Models for Time-Series Forecasting](https://mlsanity.com/paper/2609.15344/) (2026)

## Abstract (arXiv)

Time series data are ubiquitous across diverse real-world applications, making time series analysis critically important. Traditional approaches are largely task-specific, offering limited functionality and poor transferability. In recent years, foundation models have revolutionized NLP and CV with their remarkable cross-task transferability, zero-/few-shot learning capabilities, and multimodal integration capacity. This success has motivated increasing efforts to explore foundation models for addressing time series modeling challenges. Although some tutorials and surveys were published in the early stages of this field, the rapid pace of recent developments necessitates a more comprehensive and in-depth synthesis to cover the latest advances. Our survey aims to fill this gap by introducing a modality-aware, challenge-oriented perspective, which reveals how foundation models pre-trained on different modalities face distinct hurdles when adapted to time series tasks. Building on this perspective, we propose a taxonomy of existing works organized by pre-training modality (time series, language, and vision), analyze modality-specific challenges and categorize corresponding solutions, discussing their advantages and limitations. Beyond this, we review real-world applications to illustrate domain-specific advancements, provide open-source codes, and conclude with potential future research directions in this rapidly evolving field.