metadata
license: other
pipeline_tag: time-series-forecasting
library_name: pytorch
tags:
- time-series
- anomaly-detection
- LLM
datasets:
- NIPS-TS-SWAN
- MSL
- SMD
- SWaT
- PSM
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).
The model was presented in the paper: TriP-LLM: A Tri-Branch Patch-wise Large Language Model Framework for Time-Series Anomaly Detection
Model Description
- Name: TriP-LLM
- Task: Time-Series Anomaly Detection
- Framework: PyTorch
- Repository: GitHub – YYZStart/TriP-LLM
Usage
To get started with TriP-LLM, you can follow the installation and usage instructions from the GitHub repository.
Installation
We conducted our experiments using PyTorch 2.4.1, Python 3.11 and CUDA 12.4.
pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu124
To install the required dependencies and set up the environment, run the following commands:
git clone https://github.com/YYZStart/TriP-LLM.git
cd TriP-LLM
pip install -r requirements.txt
Reproduce Experiments
You can reproduce our main experiments with:
python main.py
📎 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},
year={2025},
eprint={2508.00047},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2508.00047},
}