https://github.com/salesforce/etsformer

PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
https://github.com/salesforce/etsformer

deep-learning exponential-smoothing forecasting interpretable-machine-learning pytorch time-series time-series-decomposition time-series-forecasting transformers

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PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

• Host: GitHub
• URL: https://github.com/salesforce/etsformer
• Owner: salesforce
• License: bsd-3-clause
• Created: 2022-02-15T12:43:47.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-11-12T06:22:18.000Z (about 2 years ago)
• Last Synced: 2024-04-08T00:12:45.702Z (7 months ago)
• Topics: deep-learning, exponential-smoothing, forecasting, interpretable-machine-learning, pytorch, time-series, time-series-decomposition, time-series-forecasting, transformers
• Language: Python
• Homepage:
• Size: 459 KB
• Stars: 233
• Watchers: 9
• Forks: 36
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt
  • Code of conduct: CODE_OF_CONDUCT.md
  • Codeowners: CODEOWNERS
  • Security: SECURITY.md

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README

        
# ETSformer: Exponential Smoothing Transformers for Time-series Forecasting









Figure 1. Overall ETSformer Architecture.



Official PyTorch code repository for the [ETSformer paper](https://arxiv.org/abs/2202.01381). Check out our [blog post](https://blog.salesforceairesearch.com/etsformer-time-series-forecasting/)!

* ETSformer is a novel time-series Transformer architecture which exploits the principle of exponential smoothing in improving

Transformers for timeseries forecasting.

* ETSformer is inspired by the classical exponential smoothing methods in

time-series forecasting, leveraging the novel exponential smoothing attention (ESA) and frequency attention (FA) to

replace the self-attention mechanism in vanilla Transformers, thus improving both accuracy and efficiency.

## Requirements

1. Install Python 3.8, and the required dependencies.

2. Required dependencies can be installed by: ```pip install -r requirements.txt```

## Data

* Pre-processed datasets can be downloaded from the following

  links, [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/e1ccfff39ad541908bae/)

  or [Google Drive](https://drive.google.com/drive/folders/1ZOYpTUa82_jCcxIdTmyr0LXQfvaM9vIy?usp=sharing), as obtained

  from [Autoformer's](https://github.com/thuml/Autoformer) GitHub repository.

* Place the downloaded datasets into the `dataset/` folder, e.g. `dataset/ETT-small/ETTm2.csv`.

## Usage

1. Install the required dependencies.

2. Download data as above, and place them in the folder, `dataset/`.

3. Train the model. We provide the experiment scripts of all benchmarks under the folder `./scripts`,

   e.g. `./scripts/ETTm2.sh`. You might have to change permissions on the script files by running`chmod u+x scripts/*`.

4. The script for grid search is also provided, and can be run by `./grid_search.sh`.

## Acknowledgements

The implementation of ETSformer relies on resources from the following codebases and repositories, we thank the original

authors for open-sourcing their work.

* https://github.com/thuml/Autoformer

* https://github.com/zhouhaoyi/Informer2020

## Citation

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

@article{woo2022etsformer,

    title={ETSformer: Exponential Smoothing Transformers for Time-series Forecasting},

    author={Gerald Woo and Chenghao Liu and Doyen Sahoo and Akshat Kumar and Steven C. H. Hoi},

    year={2022},

    url={https://arxiv.org/abs/2202.01381},

}