salesforce/ETSformer：的PyTorch代码：用于时间序列预测的指数平滑变换器

311

BSD 3-Clause "New" or "Revised" License

项目介绍

salesforce/ETSformer 是一个托管在 GitHub 上的开源项目，主要使用 Python 编写，采用 BSD 3-Clause "New" or "Revised" License 许可证，目前获得 311 个 Star、44 次 Fork。

仓库全名: salesforce/ETSformer
开源许可证: BSD 3-Clause "New" or "Revised" License
Star 数: 311
Fork 数: 44
未关闭 Issue: 7

README 摘要

ETSformer: Exponential Smoothing Transformers for Time-series Forecasting Figure 1. Overall ETSformer Architecture. Official PyTorch code repository for the ETSformer paper. Check out our blog post! 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…

完整内容见 GitHub 原始 README 或本页。

常见问题

ETSformer 用什么语言开发？

主要使用 Python 编写，采用 BSD 3-Clause "New" or "Revised" License 许可证，目前有 311 个 Star。

ETSformer 可以免费商用吗？

如何获取 ETSformer 的源码？

可以直接克隆仓库：git clone https://github.com/salesforce/ETSformer.git，默认分支为 main；安装与运行步骤见仓库 README。

ETSformer 最近还在维护吗？

仓库最近一次更新为 6月26日，当前有 7 个未关闭 Issue；Star 数为 311，可作为社区活跃度的参考。

关于

GitHub 原始描述（英文）

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

描述取自 GitHub 官方 API，保持仓库作者书写的原文。

简要分析

热度分

405

价值分

200

活跃状态

归档

deep-learning exponential-smoothing forecasting interpretable-machine-learning pytorch time-series time-series-decomposition time-series-forecasting transformers

GitHub

语言占比

Python

Shell

贡献者

gorold

jimjag

sfdc-ospo-bot

README

ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

Figure 1. Overall ETSformer Architecture.

Official PyTorch code repository for the ETSformer paper. Check out our blog post!

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

Install Python 3.8, and the required dependencies.
Required dependencies can be installed by: pip install -r requirements.txt

Data

Pre-processed datasets can be downloaded from the following links, Tsinghua Cloud or Google Drive, as obtained from Autoformer’s GitHub repository.
Place the downloaded datasets into the dataset/ folder, e.g. dataset/ETT-small/ETTm2.csv.

Usage

Install the required dependencies.
Download data as above, and place them in the folder, dataset/.
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 runningchmod u+x scripts/*.
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.

Citation

Please consider citing if you find this code useful to your research.

@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},
}

ETSformer