{"id":37667599,"url":"https://github.com/timeeval/timeeval-gui","last_synced_at":"2026-01-16T12:00:22.267Z","repository":{"id":41840149,"uuid":"473219611","full_name":"TimeEval/TimeEval-GUI","owner":"TimeEval","description":"[Read-Only Mirror]  Benchmarking Toolkit for Time Series Anomaly Detection Algorithms using TimeEval and 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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["benchmark-framework","benchmarking","jupyter-notebooks","numpy","pandas","python3","streamlit","time-series","time-series-analysis","time-series-anomaly-detection"],"created_at":"2026-01-16T12:00:18.037Z","updated_at":"2026-01-16T12:00:22.139Z","avatar_url":"https://github.com/TimeEval.png","language":"Python","readme":"\u003cdiv align=\"center\"\u003e\n\u003cimg width=\"100px\" src=\"timeeval-icon.png\" alt=\"TimeEval logo\"/\u003e\n\u003ch1 align=\"center\"\u003eTimeEval GUI / Toolkit\u003c/h1\u003e\n\u003cp\u003e\nA Benchmarking Toolkit for Time Series Anomaly Detection Algorithms\n\u003c/p\u003e\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n![python version 3.7|3.8|3.9](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue)\n\n\u003c/div\u003e\n\n\u003e If you use our artifacts, please consider [citing our papers](#Citation).\n\nThis repository hosts an extensible, scalable and automatic benchmarking toolkit for time series anomaly detection algorithms.\nTimeEval includes an extensive data generator and supports both interactive and batch evaluation scenarios.\nWith our novel toolkit, we aim to ease the evaluation effort and help the community to provide more meaningful evaluations.\n\nThe following picture shows the architecture of the TimeEval Toolkit:\n\n\u003cdiv align=\"center\"\u003e\n\n![TimeEval architecture](./doc/figures/timeeval-architecture.png)\n\n\u003c/div\u003e\n\nIt consists of four main components: a visual frontend for interactive experiments, the Python API to programmatically configure systematic batch experiments, the dataset generator GutenTAG, and the core evaluation engine (Time)Eval.\nWhile the frontend is hosted in this repository, GutenTAG and Eval are hosted in separate repositories.\nThose repositories also include their respective Python APIs:\n\n[![GutenTAG Badge](https://img.shields.io/badge/Repository-GutenTAG-blue?style=for-the-badge)](https://github.com/TimeEval/gutentag)\n[![Eval Badge](https://img.shields.io/badge/Repository-Eval-blue?style=for-the-badge)](https://github.com/TimeEval/timeeval)\n\nAs initial resources for evaluations, we provide over 1,000 benchmark datasets and an increasing number of time series anomaly detection algorithms (over 70):\n\n[![Datasets Badge](https://img.shields.io/badge/Repository-Datasets-3a4750?style=for-the-badge)](https://timeeval.github.io/evaluation-paper/notebooks/Datasets.html)\n[![Algorithms Badge](https://img.shields.io/badge/Repository-Algorithms-3a4750?style=for-the-badge)](https://github.com/TimeEval/TimeEval-algorithms)\n\n## Installation and Usage (tl;dr)\n\nTimeEval is tested on Linux and Mac operating systems and supports Python 3.7 until 3.9.\nWe don't support Python 3.10 or higher at the moment because downstream libraries are incompatible.\n\n\u003e We haven't tested if TimeEval runs on Windows.\n\u003e If you use Windows, please help us and test if TimeEval runs correctly.\n\u003e If there are any issues, don't hesitate to contact us.\n\nBy default, TimeEval does not automatically download all available algorithms (Docker images), because there are just too many.\nHowever, you can download them easily [from our registry](https://github.com/orgs/TimeEval/packages?repo_name=TimeEval-algorithms) using docker.\nPlease download the correct tag for the algorithm, compatible with your version of TimeEval:\n\n```bash\ndocker pull ghcr.io/timeeval/kmeans:0.3.0\n```\n\nAfter you have downloaded the algorithm images, you need to restart the GUI, so that it can find the new images.\n\n### Web frontend\n\n```shell\n# install all dependencies\nmake install\n\n# execute streamlit and display frontend in default browser\nmake run\n```\n\nScreenshots of web frontend:\n\n![GutenTAG page](./doc/figures/gutentag.png)\n![Eval page](./doc/figures/eval.png)\n![Results page](./doc/figures/results.png)\n\n### Python APIs\n\nInstall the required components using pip:\n\n```bash\n# eval component:\npip install timeeval\n\n# dataset generator component:\npip install timeeval-gutentag\n```\n\nFor usage instructions of the respective Python APIs, please consider the project's documentation:\n\n[![GutenTAG Badge](https://img.shields.io/badge/Repository-GutenTAG-blue?style=for-the-badge)](https://github.com/TimeEval/gutentag)\n[![Eval Badge](https://img.shields.io/badge/Repository-Eval-blue?style=for-the-badge)](https://github.com/TimeEval/timeeval)\n\n## Citation\n\nIf you use the TimeEval toolkit or any of its components in your project or research, please cite our demonstration paper:\n\n\u003e Phillip Wenig, Sebastian Schmidl, and Thorsten Papenbrock.\n\u003e TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms. PVLDB, 15(12): 3678 - 3681, 2022.\n\u003e doi:[10.14778/3554821.3554873](https://doi.org/10.14778/3554821.3554873)\n\nIf you use our evaluation results or our benchmark datasets and algorithms, please cite our evaluation paper:\n\n\u003e Sebastian Schmidl, Phillip Wenig, and Thorsten Papenbrock.\n\u003e Anomaly Detection in Time Series: A Comprehensive Evaluation. PVLDB, 15(9): 1779 - 1797, 2022.\n\u003e doi:[10.14778/3538598.3538602](https://doi.org/10.14778/3538598.3538602)\n\nYou can use the following BibTeX entries:\n\n```bibtex\n@article{WenigEtAl2022TimeEval,\n  title = {TimeEval: {{A}} Benchmarking Toolkit for Time Series Anomaly Detection Algorithms},\n  author = {Wenig, Phillip and Schmidl, Sebastian and Papenbrock, Thorsten},\n  date = {2022},\n  journaltitle = {Proceedings of the {{VLDB Endowment}} ({{PVLDB}})},\n  volume = {15},\n  number = {12},\n  pages = {3678--3681},\n  doi = {10.14778/3554821.3554873}\n}\n@article{SchmidlEtAl2022Anomaly,\n  title = {Anomaly Detection in Time Series: {{A}} Comprehensive Evaluation},\n  author = {Schmidl, Sebastian and Wenig, Phillip and Papenbrock, Thorsten},\n  date = {2022},\n  journaltitle = {Proceedings of the {{VLDB Endowment}} ({{PVLDB}})},\n  volume = {15},\n  number = {9},\n  pages = {1779--1797},\n  doi = {10.14778/3538598.3538602}\n}\n```\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftimeeval%2Ftimeeval-gui","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftimeeval%2Ftimeeval-gui","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftimeeval%2Ftimeeval-gui/lists"}