ETSformer

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

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# ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

<img src=".\pics\etsformer.png" width = "700" alt="" align=center />
 
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.
<pre>@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},
}</pre>

ETSformer

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