CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Official PyTorch code repository for the CoST paper. Required dependencies can be installed by:

pip install -r requirements.txt

The datasets can be obtained and put into datasets/ folder in the following way:

• 3 ETT datasets should be placed at datasets/ETTh1.csv, datasets/ETTh2.csv and datasets/ETTm1.csv.
• Electricity dataset placed at datasets/LD2011_2014.txt and run electricity.py.
• Weather dataset (link from Informer repository) placed at datasets/WTH.csv
• M5 dataset place calendar.csv, sales_train_validation.csv, sales_train_evaluation.csv, sales_test_validation.csv and sales_test_evaluation.csv at datasets/ and run m5.py.

To train and evaluate CoST on a dataset, run the following command:

python train.py <dataset_name> <run_name> --archive <archive> --batch-size <batch_size> --repr-dims <repr_dims> --gpu <gpu> --eval

The detailed descriptions about the arguments are as following:

(For descriptions of more arguments, run python train.py -h.)

After training and evaluation, the trained encoder, output and evaluation metrics can be found in training/<DatasetName>/<RunName>_<Date>_<Time>/.

Scripts: The scripts for reproduction are provided in scripts/ folder.

Q: ValueError: Found array with dim 4. StandardScaler expected <= 2.

A: Please install the appropriate package requirements as found in requirements.txt, in particular, scikit_learn==0.24.1.

The implementation of CoST relies on resources from the following codebases and repositories, we thank the original authors for open-sourcing their work.

• https://github.com/yuezhihan/ts2vec
• https://github.com/zhouhaoyi/Informer2020

Please consider citing if you find this code useful to your research.

@inproceedings{
    woo2022cost,
    title={Co{ST}: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting},
    author={Gerald Woo and Chenghao Liu and Doyen Sahoo and Akshat Kumar and Steven Hoi},
    booktitle={International Conference on Learning Representations},
    year={2022},
    url={https://openreview.net/forum?id=PilZY3omXV2}
}