# TriP-LLM

This is the official checkpoints release for the  **TriP-LLM**, a novel framework for unsupervised anomaly detection in multivariate time-series data using pretrained Large Language Models (LLMs).

## Model Description
- **Name**: TriP-LLM  
- **Task**: Time-Series Anomaly Detection  
- **Framework**: PyTorch 
- **Repository**: [GitHub – YYZStart/TriP-LLM](https://github.com/YYZStart/TriP-LLM)


## Usage

Please refer to our [GitHub repository](https://github.com/YYZStart/TriP-LLM)  
for model definitions, training code, and usage examples.


## 📎 Citation

If you find this repository useful for your research, please cite our paper:

```bibtex
@misc{TriPLLM,
      title={TriP-LLM: A Tri-Branch Patch-wise Large Language Model Framework for Time-Series Anomaly Detection}, 
      author={Yuan-Cheng Yu and Yen-Chieh Ouyang and Chun-An Lin},
      journal={IEEE Access},
      year={2025},
      pages={168643-168653}
}
```