| # 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} | |
| } | |
| ``` |