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

Usage

Please refer to our GitHub repository
for model definitions, training code, and usage examples.

📎 Citation

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

@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}
}