Metadata-Version: 2.1
Name: tsfel
Version: 0.1.2
Summary: Library for time series feature extraction
Home-page: https://github.com/fraunhoferportugal/tsfel/
Author: Fraunhofer Portugal
License: UNKNOWN
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        # 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 tsfel
        ```
        
        ## Includes a comprehensive number of features
        TSFEL is optimized for time series and **automatically extracts over 60 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
        
        ## Get started
        The code below extracts all the available features on an example dataset file.
        
        ```python
        import tsfel
        import pandas as pd
        
        # load dataset
        df = pd.read_csv('Dataset.txt')
        
        # Retrieves a pre-defined feature configuration file to extract all available features
        cfg = tsfel.get_features_by_domain()
        
        # Extract features
        X = tsfel.time_series_features_extractor(cfg, df)
        ```
        
        ## Acknowledgements
        We would like to acknowledge the financial support obtained from the project Total Integrated and Predictive Manufacturing System Platform for Industry 4.0, co-funded by Portugal 2020, framed under the COMPETE 2020 (Operational Programme  Competitiveness and Internationalization) and European Regional Development Fund (ERDF) from European Union (EU), with operation code POCI-01-0247-FEDER-038436.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
