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gorold
35 lines
1.8 KiB
Markdown
35 lines
1.8 KiB
Markdown
# ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
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<img src="pics/etsformer.png" width="700">
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Official PyTorch code repository for the [ETSformer paper](https://arxiv.org/abs/2202.01381).
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## Requirements
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Required dependencies can be installed by:
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```bash
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pip install -r requirements.txt
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```
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## Data
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* 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.
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* Place the downloaded datasets into the `dataset/` folder, e.g. `dataset/ETT-small/ETTm2.csv`.
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## Usage
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1. Install Python 3.8, and the required dependencies.
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2. Download data as above, and place them in the folder, `dataset/`.
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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/*`.
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4. The script for grid search is also provided, and can be run by `./grid_search.sh`.
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## Acknowledgements
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The implementation of ETSformer relies on resources from the following codebases and repositories, we thank the original authors for open-sourcing their work.
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* https://github.com/thuml/Autoformer
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* https://github.com/zhouhaoyi/Informer2020
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## Citation
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Please consider citing if you find this code useful to your research.
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<pre>@article{woo2022etsformer,
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title={ETSformer: Exponential Smoothing Transformers for Time-series Forecasting},
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author={Gerald Woo and Chenghao Liu and Doyen Sahoo and Akshat Kumar and Steven C. H. Hoi},
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year={2022},
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url={https://arxiv.org/abs/2202.01381},
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}</pre> |