[KDD 2025] Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation

by: Peiliang Gong, Mohamed Ragab, Min Wu, Zhenghua Chen, Yongyi Su, Xiaoli Li, Daoqiang Zhang

Accepted in the 31st SIGKDD Conference on Knowledge Discovery and Data Mining - Research Track.

Requirmenets:

• Python3
• Pytorch==1.9
• Numpy==1.23.5
• scikit-learn==1.0
• Pandas==1.3.4
• skorch==0.10.0
• openpyxl==3.0.7
• Wandb=0.12.7

Datasets

Available Datasets

We used three public datasets in this study. We also provide the preprocessed versions as follows:

• SSC
• UCIHAR
• MFD

Training procedure

The experiments are organised in a hierarchical way such that:

• Several experiments are collected under one directory assigned by --exp_name.

Training a model

To train a model:

python trainers/tta_trainer.py  --exp_name All_trg  \
                --da_method ACCUP \
                --dataset HAR \
                --backbone CNN \
                --num_runs 3 \

Citation

If you found this work useful for you, please consider citing it.

@inproceedings{accup,
  author = {Gong, Peiliang and Ragab, Mohamed and Wu, Min and Chen, Zhenghua and Su, Yongyi and Li, Xiaoli},
  title = {Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation},
  booktitle={31st SIGKDD Conference on Knowledge Discovery and Data Mining - Research Track},
  year = {2025}
}