/Cross-Reconstruction-Transformer

The official implementation for our TNNLS paper "Self-Supervised Time Series Representation Learning via Cross Reconstruction Transformer".

Primary LanguagePython

Self-Supervised Time Series Representation Learning via Cross Reconstruction Transformer

The official implementation for our TNNLS paper Self-Supervised Time Series Representation Learning via Cross Reconstruction Transformer.

Overview of Cross Reconstruction Transformer

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Getting Started

Installation

Git clone our repository, and install the required packages with the following command

git clone https://github.com/BobZwr/Cross-Reconstruction-Transformer.git
cd Cross-Reconstruction-Transformer
pip install -r requirements.txt

We use torch=1.13.0.

Training and Evaluating

We provide the sample script for training and evaluating our CRT

# For Training:
python main.py --ssl True --sl True --load True --seq_len 256 --patch_len 8 --in_dim 9 --n_classes 6

# For Testing:
python main.py --ssl False --sl False --load False --seq_len 256 --patch_len 8 --in_dim 9 --n_classes 6

We also provide a subset of HAR dataset for training and testing.

If you found the codes and datasets are useful, please cite our paper

@article{zhang2022cross,
  title={Cross reconstruction transformer for self-supervised time series representation learning},
  author={Zhang, Wenrui and Yang, Ling and Geng, Shijia and Hong, Shenda},
  journal={arXiv preprint arXiv:2205.09928},
  year={2022}
}