fraunhoferportugal / Tsfel
Licence: bsd-3-clause
An intuitive library to extract features from time series
Stars: ✭ 202
Programming Languages
python
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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
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.
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 |
| 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
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.
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