| # TriP-LLM |
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| 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). |
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| ## Model Description |
| - **Name**: TriP-LLM |
| - **Task**: Time-Series Anomaly Detection |
| - **Framework**: PyTorch |
| - **Repository**: [GitHub – YYZStart/TriP-LLM](https://github.com/YYZStart/TriP-LLM) |
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| ## Usage |
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| Please refer to our [GitHub repository](https://github.com/YYZStart/TriP-LLM) |
| for model definitions, training code, and usage examples. |
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| ## 📎 Citation |
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| 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} |
| } |
| ``` |
