Can Multimodal LLMs Perform Time Series Anomaly Detection?

The code of WWW (The Web Conference)'25 paper: "Can Multimodal LLMs Perform Time Series Anomaly Detection?"

🕵️‍♂️ VisualTimeAnomaly

Left: the workflow of VisualTimeAnomaly. Right: the performance comparison across various setting.

🏆 Contributions

• The first comprehensive benchmark for multimodal LLMs (MLLMs) in time series anomaly detection (TSAD), covering diverse scenarios (univariate, multivariate, irregular) and varying anomaly granularities (point-, range-, variate-wise).
• Several critical insights significantly advance the understanding of both MLLMs and TSAD.
• We construct a large-scale dataset including 12.4k time series images, and release the dateset and code to foster future research.

🔎 Findings

• MLLMs detect range- and variate-wise anomalies more effectively than point-wise anomalies;
• MLLMs are highly robust to irregular time series, even with 25% of the data missing;
• Open-source MLLMs perform comparably to proprietary models in TSAD. While open-source MLLMs excel on univariate time series, proprietary MLLMs demonstrate superior effectiveness on multivariate time series.

⚙️ Getting Started

Environment

• python 3.10.14
• torch 2.4.1
• numpy 1.26.4
• transformers 4.49.0.dev0
• huggingface-hub 0.24.7
• openai 1.44.0
• google-generativeai 0.8.3

Dataset

Enter src folder.

If you want to generate all datasets, execute the below script:

./generator.sh

If you want to generate a specific dataset, execute the below script:

python generator.py --category $category --scenario $scenario --anomaly_type $anomaly_type --num_ts $num_ts.

For example, generate 100 univaraite time series images for global anomalies:

python generator.py --category synthetic --scenario univariate --anomaly_type global --num_ts 100

Run

Enter src folder.

If you want to run MLLMs on all datasets, execute the below script:

./test.sh

If you want to run a MLLM on a specific dataset, execute the below script:

python main.py --category $category --scenario $scenario --model_name $model_name --data $data

For example, run GPT-4o on univaraite time series scenario with global anomalies:

python main.py --category synthetic --scenario univariate --model_name gpt-4o --data global

Acknowledgement

We sincerely appreciate the following github repo for the code base and datasets:

https://github.com/Rose-STL-Lab/AnomLLM

https://github.com/datamllab/tods/tree/benchmark

📝 Citation

If you find our work useful, please cite the below paper:

@article{xu2025can,
  title={Can Multimodal LLMs Perform Time Series Anomaly Detection?},
  author={Xu, Xiongxiao and Wang, Haoran and Liang, Yueqing and Yu, Philip S and Zhao, Yue and Shu, Kai},
  journal={arXiv preprint arXiv:2502.17812},
  year={2025}
}