{"id":18950154,"url":"https://github.com/salesforce/etsformer","last_synced_at":"2025-04-07T13:07:57.107Z","repository":{"id":37998470,"uuid":"459574227","full_name":"salesforce/ETSformer","owner":"salesforce","description":"PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting","archived":false,"fork":false,"pushed_at":"2024-05-10T09:13:31.000Z","size":470,"stargazers_count":274,"open_issues_count":6,"forks_count":44,"subscribers_count":7,"default_branch":"main","last_synced_at":"2025-03-31T11:05:33.923Z","etag":null,"topics":["deep-learning","exponential-smoothing","forecasting","interpretable-machine-learning","pytorch","time-series","time-series-decomposition","time-series-forecasting","transformers"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/salesforce.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":"CODEOWNERS","security":"SECURITY.md","support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-02-15T12:43:47.000Z","updated_at":"2025-03-27T01:44:59.000Z","dependencies_parsed_at":"2024-12-17T09:06:06.416Z","dependency_job_id":"77c15d84-8fa7-426a-bed6-a54893dcf3a2","html_url":"https://github.com/salesforce/ETSformer","commit_stats":{"total_commits":15,"total_committers":1,"mean_commits":15.0,"dds":0.0,"last_synced_commit":"2566f689d022ec39acdcecd829898fcea17ba6dd"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/salesforce%2FETSformer","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/salesforce%2FETSformer/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/salesforce%2FETSformer/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/salesforce%2FETSformer/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/salesforce","download_url":"https://codeload.github.com/salesforce/ETSformer/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247657281,"owners_count":20974345,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","exponential-smoothing","forecasting","interpretable-machine-learning","pytorch","time-series","time-series-decomposition","time-series-forecasting","transformers"],"created_at":"2024-11-08T13:21:16.358Z","updated_at":"2025-04-07T13:07:57.087Z","avatar_url":"https://github.com/salesforce.png","language":"Python","readme":"# ETSformer: Exponential Smoothing Transformers for Time-series Forecasting\n\n\u003cp align=\"center\"\u003e\n\u003cimg src=\".\\pics\\etsformer.png\" width = \"700\" alt=\"\" align=center /\u003e\n\u003cbr\u003e\u003cbr\u003e\n\u003cb\u003eFigure 1.\u003c/b\u003e Overall ETSformer Architecture.\n\u003c/p\u003e\n\nOfficial 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/)!\n\n* ETSformer is a novel time-series Transformer architecture which exploits the principle of exponential smoothing in improving\nTransformers for timeseries forecasting.\n* ETSformer is inspired by the classical exponential smoothing methods in\ntime-series forecasting, leveraging the novel exponential smoothing attention (ESA) and frequency attention (FA) to\nreplace the self-attention mechanism in vanilla Transformers, thus improving both accuracy and efficiency.\n\n## Requirements\n\n1. Install Python 3.8, and the required dependencies.\n2. Required dependencies can be installed by: ```pip install -r requirements.txt```\n\n## Data\n\n* Pre-processed datasets can be downloaded from the following\n  links, [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/e1ccfff39ad541908bae/)\n  or [Google Drive](https://drive.google.com/drive/folders/1ZOYpTUa82_jCcxIdTmyr0LXQfvaM9vIy?usp=sharing), as obtained\n  from [Autoformer's](https://github.com/thuml/Autoformer) GitHub repository.\n* Place the downloaded datasets into the `dataset/` folder, e.g. `dataset/ETT-small/ETTm2.csv`.\n\n## Usage\n\n1. Install the required dependencies.\n2. Download data as above, and place them in the folder, `dataset/`.\n3. Train the model. We provide the experiment scripts of all benchmarks under the folder `./scripts`,\n   e.g. `./scripts/ETTm2.sh`. You might have to change permissions on the script files by running`chmod u+x scripts/*`.\n4. The script for grid search is also provided, and can be run by `./grid_search.sh`.\n\n## Acknowledgements\n\nThe implementation of ETSformer relies on resources from the following codebases and repositories, we thank the original\nauthors for open-sourcing their work.\n\n* https://github.com/thuml/Autoformer\n* https://github.com/zhouhaoyi/Informer2020\n\n## Citation\n\nPlease consider citing if you find this code useful to your research.\n\u003cpre\u003e@article{woo2022etsformer,\n    title={ETSformer: Exponential Smoothing Transformers for Time-series Forecasting},\n    author={Gerald Woo and Chenghao Liu and Doyen Sahoo and Akshat Kumar and Steven C. H. Hoi},\n    year={2022},\n    url={https://arxiv.org/abs/2202.01381},\n}\u003c/pre\u003e","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsalesforce%2Fetsformer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsalesforce%2Fetsformer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsalesforce%2Fetsformer/lists"}