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A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.
| Date | Stars |
|---|---|
| 2026-07-24 | 2991 |
| 2026-07-25 | 2990 |
| 2026-07-28 | 2990 |
| 2026-07-30 | 2990 |
| 2026-08-06 | 2990 |
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# Transformers in Time Series
[](https://awesome.re)
[](https://badges.pufler.dev/visits/qingsongedu/time-series-transformers-review)
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A professionally curated list of awesome resources (paper, code, data, etc.) on **Transformers in Time Series**, which is first work to comprehensively and systematically summarize the recent advances of Transformers for modeling time series data to the best of our knowledge.
We will continue to update this list with newest resources. If you found any missed resources (paper/code) or errors, please feel free to open an issue or make a pull request.
For general **AI for Time Series (AI4TS)** Papers, Tutorials, and Surveys at the **Top AI Conferences and Journals**, please check [This Repo](https://github.com/qingsongedu/awesome-AI-for-time-series-papers).
For general **Recent AI Advances: Tutorials and Surveys in various areas (DL, ML, DM, CV, NLP, Speech, etc.)** at the **Top AI Conferences and Journals**, please check [This Repo](https://github.com/qingsongedu/awesome-AI-tutorials-surveys).
## Survey paper
[**Transformers in Time Series: A Survey**](https://arxiv.org/abs/2202.07125) (IJCAI'23 Survey Track)
[Qingsong Wen](https://sites.google.com/site/qingsongwen8/), Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, [Junchi Yan](https://thinklab.sjtu.edu.cn/) and [Liang Sun](https://scholar.google.com/citations?user=8JbrsgUAAAAJ&hl=en).
#### If you find this repository helpful for your work, please kindly cite our survey paper.
```bibtex
@inproceedings{wen2023transformers,
title={Transformers in time series: A survey},
author={Wen, Qingsong and Zhou, Tian and Zhang, Chaoli and Chen, Weiqi and Ma, Ziqing and Yan, Junchi and Sun, Liang},
booktitle={International Joint Conference on Artificial Intelligence(IJCAI)},
year={2023}
}
```
## Taxonomy of Transformers for time series modeling
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<img src="TS_Xformer_V2.jpg" width=700 align=middle> <br />
## Application Domains of Time Series Transformers
[\[official code\]]()
### Transformers in Forecasting
#### Time Series Forecasting
* CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting, in *ICLR* 2024. [\[paper\]](https://openreview.net/forum?id=MJksrOhurE) [\[official code\]](https://github.com/wxie9/card)
* Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting, in *ICLR* 2024. [\[paper\]](https://openreview.net/forum?id=lJkOCMP2aW) [\[official code\]](https://github.com/decisionintelligence/pathformer)
* GAFormer: Enhancing Timeseries Transformers Through Group-Aware Embeddings, in *ICLR* 2024. [\[paper\]](https://openreview.net/forum?id=c56TWtYp0W)
* Transformer-Modulated Diffusion Models for Probabilistic Multivariate Time Series Forecasting, in *ICLR* 2024. [\[paper\]](https://openreview.net/forum?id=qae04YACHs)
* iTransformer: Inverted Transformers Are Effective for Time Series Forecasting, in *ICLR* 2024. [\[paper\]](https://openreview.net/forum?id=JePfAI8fah)
* Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting, in *AAAI* 2024. [\[paper\]]()
* Latent Diffusion Transformer for Probabilistic Time Series Forecasting, in *AAAI* 2024. [\[paper\]]()
* BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis, in *NeurIPS* 2023. [\[paper\]](https://neurips.cc/virtual/2023/poster/69976)
* ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling, in *NeurIPS* 2023. [\[paper\]](https://neurips.cc/virtual/2023/poster/71304)
* A Time Series isExcerpt of 19,962 characters
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Qingsong Wen · Squirrel Ai Learning · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:81405262fedb855e, topic:awesome, desc:curated list, readme:curated list
matched fp:81405262fedb855e, topic:deep-learning
matched fp:81405262fedb855e, topic:transformer