Transformers时间序列预测Exponential Smoothing Transformers（Python完整源码）

# 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>