Metadata-Version: 2.1
Name: tsfel
Version: 0.1.3
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)
        ```
        
        ## Available features
        
        #### Statistical domain
        | Features                   | Computational Cost |
        |----------------------------|:------------------:|
        | ECDF                       |          1         |
        | ECDF Percentile            |          1         |
        | ECDF Percentile Count      |          1         |
        | ECDF Slope                 |          1         |
        | Histogram                  |          1         |
        | Interquartile range        |          1         |
        | Kurtosis                   |          1         |
        | Max                        |          1         |
        | Mean                       |          1         |
        | Mean absolute deviation    |          1         |
        | Median                     |          1         |
        | Median absolute deviation  |          1         |
        | Min                        |          1         |
        | Root mean square           |          1         |
        | Skewness                   |          1         |
        | Standard deviation         |          1         |
        | Variance                   |          1         |
        
        
        #### Temporal domain
        | Features                   | Computational Cost |
        |----------------------------|:------------------:|
        | Absolute energy            |          1         |
        | Area under the curve       |          1         |
        | Autocorrelation            |          1         |
        | Centroid                   |          1         |
        | Entropy                    |          1         |
        | Mean absolute diff         |          1         |
        | Mean diff                  |          1         |
        | Median absolute diff       |          1         |
        | Median diff                |          1         |
        | Negative turning points    |          1         |
        | Peak to peak distance      |          1         |
        | Positive turning points    |          1         |
        | Signal distance            |          1         |
        | Slope                      |          1         |
        | Sum absolute diff          |          1         |
        | Total energy               |          1         |
        | Zero crossing rate         |          1         |
        | Neighbourhood peaks        |          1         |
        
        
        #### Spectral domain
        | Features                          | Computational Cost |
        |-----------------------------------|:------------------:|
        | FFT mean coefficient              |          1         |
        | Fundamental frequency             |          1         |
        | Human range energy                |          2         |
        | LPCC                              |          1         |
        | MFCC                              |          1         |
        | Max power spectrum                |          1         |
        | Maximum frequency                 |          1         |
        | Median frequency                  |          1         |
        | Power bandwidth                   |          1         |
        | Spectral centroid                 |          2         |
        | Spectral decrease                 |          1         |
        | Spectral distance                 |          1         |
        | Spectral entropy                  |          1         |
        | Spectral kurtosis                 |          2         |
        | Spectral positive turning points  |          1         |
        | Spectral roll-off                 |          1         |
        | Spectral roll-on                  |          1         |
        | Spectral skewness                 |          2         |
        | Spectral slope                    |          1         |
        | Spectral spread                   |          2         |
        | Spectral variation                |          1         |
        | Wavelet absolute mean             |          2         |
        | Wavelet energy                    |          2         |
        | Wavelet standard deviation        |          2         |
        | Wavelet entropy                   |          2         |
        | Wavelet variance                  |          2         |
        
        
        ## Citing
        When using TSFEL please cite the following publication:
        
        Barandas, Marília and Folgado, Duarte, et al. "*TSFEL: Time Series Feature Extraction Library.*" SoftwareX 11 (2020). [https://doi.org/10.1016/j.softx.2020.100456](https://doi.org/10.1016/j.softx.2020.100456)
        
        ## 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
