Package: tsfel
Time Series Feature Extraction LibraryIntuitive time series feature extractionThis repository hosts theTSFEL - Time Series Feature Extraction Librarypython 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:OnlineIt does not requires installation as it relies on Google Colabs and a user interface provided by Google SheetsOfflineAdvanced users can take full potential of TSFEL by installing as a python packagepipinstalltsfelIncludes a comprehensive number of featuresTSFEL is optimized for time series andautomatically extracts over 60 different features on the statistical, temporal and spectral domains.FunctionalitiesIntuitive, fast deployment and reproducible: interactive UI for feature selection and customizationComputational complexity evaluation: estimate the computational effort before extracting featuresComprehensive documentation: each feature extraction method has a detailed explanationUnit tested: we provide unit tests for each featureEasily extended: adding new features is easy and we encourage you to contribute with your custom featuresGet startedThe code below extracts all the available features on an example dataset file.importtsfelimportpandasaspd# load datasetdf=pd.read_csv('Dataset.txt')# Retrieves a pre-defined feature configuration file to extract all available featurescfg=tsfel.get_features_by_domain()# Extract featuresX=tsfel.time_series_features_extractor(cfg,df)Available featuresStatistical domainFeaturesComputational CostAbsolute energy1Average power1ECDF1ECDF Percentile1ECDF Percentile Count1Entropy1Histogram1Interquartile range1Kurtosis1Max1Mean1Mean absolute deviation1Median1Median absolute deviation1Min1Root mean square1Skewness1Standard deviation1Variance1Temporal domainFeaturesComputational CostArea under the curve1Autocorrelation1Centroid1Mean absolute diff1Mean diff1Median absolute diff1Median diff1Negative turning points1Peak to peak distance1Positive turning points1Signal distance1Slope1Sum absolute diff1Zero crossing rate1Neighbourhood peaks1Spectral domainFeaturesComputational CostFFT mean coefficient1Fundamental frequency1Human range energy2LPCC1MFCC1Max power spectrum1Maximum frequency1Median frequency1Power bandwidth1Spectral centroid2Spectral decrease1Spectral distance1Spectral entropy1Spectral kurtosis2Spectral positive turning points1Spectral roll-off1Spectral roll-on1Spectral skewness2Spectral slope1Spectral spread2Spectral variation1Wavelet absolute mean2Wavelet energy2Wavelet standard deviation2Wavelet entropy2Wavelet variance2CitingWhen 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.2020.100456AcknowledgementsWe 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.