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:
It does not requires installation as it relies on Google Colabs and a user interface provided by Google Sheets
Advanced users can take full potential of TSFEL by installing as a python package
pip install tsfelTSFEL is optimized for time series and automatically extracts over 60 different features on the statistical, temporal and spectral domains.
- 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
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)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.