Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A professional list on Large (Language) Models and Foundation Models (LLM, LM, FM) for Time Series, Spatiotemporal, and Event Data.
| Date | Stars |
|---|---|
| 2026-07-31 | 1222 |
| 2026-08-02 | 1222 |
| 2026-08-06 | 1222 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Large (Language) Models and Foundation Models (LLM, LM, FM) for Time Series and Spatio-Temporal Data
[](https://awesome.re)
[](https://badges.pufler.dev/visits/qingsongedu/Awesome-TimeSeries-AIOps-LM-LLM)
<!--  -->
A professionally curated list of **Large (Language) Models and Foundation Models (LLM, LM, FM) for Temporal Data (Time Series, Spatio-temporal, and Event Data)** with awesome resources (paper, code, data, etc.), which aims to comprehensively and systematically summarize the recent advances to the best of our knowledge.
We will continue to update this list with the newest resources. If you find 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).
## Survey paper
[**Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook**](https://arxiv.org/abs/2310.10196)
**Authors**: Ming Jin, Qingsong Wen*, Yuxuan Liang, Chaoli Zhang, Siqiao Xue, Xue Wang, James Zhang, Yi Wang, Haifeng Chen, Xiaoli Li (IEEE Fellow), Shirui Pan*, Vincent S. Tseng (IEEE Fellow), Yu Zheng (IEEE Fellow), Lei Chen (IEEE Fellow), Hui Xiong (IEEE Fellow)
🌟 If you find this resource helpful, please consider to star this repository and cite our survey paper:
```
@article{jin2023lm4ts,
title={Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook},
author={Ming Jin and Qingsong Wen and Yuxuan Liang and Chaoli Zhang and Siqiao Xue and Xue Wang and James Zhang and Yi Wang and Haifeng Chen and Xiaoli Li and Shirui Pan and Vincent S. Tseng and Yu Zheng and Lei Chen and Hui Xiong},
journal={arXiv preprint arXiv:2310.10196},
year={2023}
}
```
## LLMs for Time Series
#### General Time Series Analysis
* Position Paper: What Can Large Language Models Tell Us about Time Series Analysis, in *ICML* 2024, [\[paper\]](https://arxiv.org/abs/2402.02713)
* Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting, *NeurIPS* 2024. [\[paper\]](https://arxiv.org/abs/2405.14252)
* Time-MMD: A New Multi-Domain Multimodal Dataset for Time Series Analysis, *NeurIPS* 2024. [\[paper\]](https://arxiv.org/abs/2406.08627) [\[official code\]](https://github.com/adityalab/time-mmd)
* From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection, *NeurIPS* 2024. [\[paper\]](https://arxiv.org/abs/2409.17515) [\[official code\]](https://github.com/ameliawong1996/From_News_to_Forecast)
* Autotimes: Autoregressive time series forecasters via large language models, *NeurIPS* 2024. [\[paper\]](https://arxiv.org/abs/2402.02370) [\[official code\]](https://github.com/thuml/AutoTimes)
* S^2IP-LLM: Semantic Space Informed Prompt Learning with LLM for Time Series Forecasting, in *ICML* 2024, [\[paper\]](https://openreview.net/forum?id=qwQVV5R8Y7)
* Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning, in *ICML* 2024, [\[paper\]](https://arxiv.org/abs/2402.04852) [\[official code\]](https://github.com/yxbian23/aLLM4TS)
* TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment, in *AAAI* 2025, [\[paper\]](https://arxiv.org/abs/2406.01638)
* Time-LLM: Time Series Forecasting by Reprogramming Large Language Models, in *ICLR* 2024, [\[paper\]](https://arxiv.org/abs/2310.01728) [\[official code\]](https://github.com/KimMeen/Time-LLM)
* TEMPO: Prompt-based Generative Pre-trained TraExcerpt of 24,379 characters
Read on GitHub
Qingsong Wen · Squirrel Ai Learning · United States
32
4
3
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7ce0f98fd3bd2e1d, topic:large-language-models, topic:foundation-models