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ETSformer

(★ 310)

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

파일 탐색기

최종 버전 다운로드 (.zip)

- __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

# 설치 가이드

1. 코드 내려받기

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

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd ETSformer

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

python run.py

파이썬 스크립트(또는 모듈)를 실행합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// 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>