tsfel 0.0.2

Library for time series feature extraction

[](https://github.com/fraunhoferportugal/tsfel/blob/master/LICENSE.txt)  [](https://colab.research.google.com/github/fraunhoferportugal/tsfel/blob/master/notebooks/TSFEL_HAR_Example.ipynb)

# Time Series Feature Extraction Library ## Intuitive time series feature extraction This 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.

Users can interact with TSFEL using two methods: ##### Online It does not requires installation as it relies on Google Colabs and a user interface provided by Google Sheets

##### Offline Advanced users can take full potential of TSFEL by installing as a python package `python pip install https://github.com/fraunhoferportugal/tsfel/archive/v0.0.2.zip `

## Includes a comprehensive number of features TSFEL is optimized for time series and automatically extracts over 50 different features on the statistical, temporal and spectral domains.

## Functionalities * Intuitive, fast deployment and reproducible: interactive UI for feature selection and customization * Computational complexity evaluation: estimate the computational effort before extracting features * Comprehensive documentation: each feature extraction method has a detailed explanation * Unit tested: we provide unit tests for each feature * Easily extended: adding new features is easy and we encourage you to contribute with your custom features

## Acknowledgements We would like to acknowledge the financial support obtained from North Portugal Regional Operational Programme (NORTE 2020), Portugal 2020 and the European Regional Development Fund (ERDF) from European Union through the project Symbiotic technology for societal efficiency gains: Deus ex Machina (DEM), NORTE-01-0145-FEDER-000026.