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wassname
59 lines
2.6 KiB
Markdown
59 lines
2.6 KiB
Markdown
Fork info: trying out and plotting the results on stock data.----------
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# ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
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<p align="center">
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<img src=".\pics\etsformer.png" width = "700" alt="" align=center />
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<br><br>
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<b>Figure 1.</b> Overall ETSformer Architecture.
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</p>
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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/)!
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* ETSformer is a novel time-series Transformer architecture which exploits the principle of exponential smoothing in improving
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Transformers for timeseries forecasting.
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* ETSformer is inspired by the classical exponential smoothing methods in
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time-series forecasting, leveraging the novel exponential smoothing attention (ESA) and frequency attention (FA) to
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replace the self-attention mechanism in vanilla Transformers, thus improving both accuracy and efficiency.
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## Requirements
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1. Install Python 3.8, and the required dependencies.
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2. Required dependencies can be installed by: ```pip install -r requirements.txt```
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## Data
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* Pre-processed datasets can be downloaded from the following
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links, [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/e1ccfff39ad541908bae/)
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or [Google Drive](https://drive.google.com/drive/folders/1ZOYpTUa82_jCcxIdTmyr0LXQfvaM9vIy?usp=sharing), as obtained
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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 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`,
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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
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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>
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