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CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting (ICLR 2022)

<img src=".\pics\CoST.png" width = "700" alt="" align=center /> Figure 1. Overall CoST Architecture.

Official PyTorch code repository for the CoST paper.

CoST is a contrastive learning method for learning disentangled seasonal-trend representations for time series forecasting.
CoST consistently outperforms state-of-the-art methods by a considerable margin, achieveing a 21.3% improvement in MSE on multivariate benchmarks.

Requirements

Install Python 3.8, and the required dependencies.
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 script from the scripts folder: ./scripts/ETT_CoST.sh (edit file permissions via chmod u+x scripts/*).

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

Alternatively, you can directly run the python scripts:

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.)

Main Results

We perform experiments on five real-world public benchmark datasets, comparing against both state-of-the-art representation learning and end-to-end forecasting approaches. CoST achieves state-of-the-art performance, beating the best performing end-to-end forecasting approach by 39.3% and 18.22% (MSE) in the multivariate and univariate settings respectively. CoST also beats next best performing feature-based approach by 21.3% and 4.71% (MSE) in the multivariate and univariate settings respectively (refer to main paper for full results).

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.

Q: How to set the --kernels parameter?

A: It should be list of space separated integers, e.g. --kernels 1 2 4. See the scripts folder for further examples.

Acknowledgements

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

Citation

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

<pre>@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} }</pre>