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

Data

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.

Usage

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:

Parameter name	Description of parameter
dataset_name	The dataset name
run_name	The folder name used to save model, output and evaluation metrics. This can be set to any word
archive	The archive name that the dataset belongs to. This can be set to forecast_csv or forecast_csv_univar
batch_size	The batch size (defaults to 8)
repr_dims	The representation dimensions (defaults to 320)
gpu	The gpu no. used for training and inference (defaults to 0)
eval	Whether to perform evaluation after training
kernels	Kernel sizes for mixture of AR experts module
alpha	Weight for loss function

(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.

FAQs

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.

Acknowledgements

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

Citation

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}
}