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ETSformer

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

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- __init__.py
- data_factory.py
- data_loader.py
- .gitignore
- __init__.py
- exp_basic.py
- exp_main.py
- - __init__.py
  - decoder.py
  - encoder.py
  - exponential_smoothing.py
  - model.py
  - modules.py
- __init__.py
- etsformer.png
- ECL.sh
- ETTm2.sh
- ETTm2_univar.sh
- Exchange.sh
- Exchange_univar.sh
- grid_search.sh
- ILI.sh
- Traffic.sh
- Weather.sh
- __init__.py
- Adam.py
- masking.py
- metrics.py
- timefeatures.py
- tools.py
CODE_OF_CONDUCT.md
CODEOWNERS
CONTRIBUTING-ARCHIVED.md
LICENSE.txt
README.md
requirements.txt
run.py
SECURITY.md

# Installation Guide

1. Get the code

git clone https://github.com/salesforce/ETSformer

Downloads the entire project code from GitHub to your computer.

cd ETSformer

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

python run.py

Runs the Python script (or module).

✅ If it runs without errors and prints output in the terminal, it worked.

// repository documentation

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# ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

<p align="center">
<img src=".\pics\etsformer.png" width = "700" alt="" align=center />
<br><br>
<b>Figure 1.</b> Overall ETSformer Architecture.
</p>

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>