{ "info": { "author": "Fraunhofer Portugal", "author_email": "", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 3" ], "description": "[![license](https://img.shields.io/github/license/mashape/apistatus.svg)](https://github.com/fraunhoferportugal/tsfel/blob/master/LICENSE.txt)\n![py368 status](https://img.shields.io/badge/python3.6.8-supported-green.svg)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/fraunhoferportugal/tsfel/blob/master/notebooks/TSFEL_HAR_Example.ipynb)\n\n# Time Series Feature Extraction Library\n## Intuitive time series feature extraction\nThis repository hosts the *TSFEL - Time Series Feature Extraction Library* python package. *TSFEL* assists researchers on exploratory feature extraction tasks on time series without requiring significant programming effort.\n\nUsers can interact with *TSFEL* using two methods:\n##### Online\nIt does not requires installation as it relies on Google Colabs and a user interface provided by Google Sheets\n\n##### Offline\nAdvanced users can take full potential of *TSFEL* by installing as a *python* package\n```python\npip install https://github.com/fraunhoferportugal/tsfel/archive/v0.0.2.zip\n```\n\n## Includes a comprehensive number of features\n*TSFEL* is optimized for time series and automatically extracts over 50 different features on the statistical, temporal and spectral domains.\n\n## Functionalities\n* **Intuitive, fast deployment and reproducible**: interactive UI for feature selection and customization\n* **Computational complexity evaluation**: estimate the computational effort before extracting features\n* **Comprehensive documentation**: each feature extraction method has a detailed explanation\n* **Unit tested**: we provide unit tests for each feature\n* **Easily extended**: adding new features is easy and we encourage 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