Welcome to TSFEL documentation!
Time Series Feature Extraction Library (TSFEL) is a Python package for efficient feature extraction from time series data. It offers a comprehensive set of feature extraction routines without requiring extensive programming effort. TSFEL automatically extracts over 65 features spanning statistical, temporal, spectral, and fractal domains.
The TSFEL project began in 2019 intending to centralize development in feature extraction methods for time series data, applicable across various fields including healthcare and industry. TSFEL is currently being used in academic and industrial projects, demonstrating its wide-ranging applicability. Built by data scientists for data scientists, TSFEL aims to streamline and enhance feature extraction processes.
Highlights
Intuitive, fast deployment, and reproducible: Easily configure your feature extraction pipeline and store the configuration file to ensure reproducibility.
Computational complexity evaluation: Estimate the computational time required for feature extraction in advance.
Comprehensive documentation: Each feature extraction method is accompanied by a detailed explanation.
Unit tested: We provide an extensive suite of unit tests for each feature to ensure accurate and reliable feature calculation.
Easily extended: Adding new features is straightforward, and we encourage contributions of custom features to the community.
Installation
Installation can be easily done with pip:
$ pip install tsfel
Get started
The code below extracts all the available features on an example dataset.
import tsfel
import pandas as pd
# load dataset
data = tsfel.datasets.load_biopluxecg()
# Retrieves a pre-defined feature configuration file to extract the temporal, statistical and spectral feature sets
cfg = tsfel.get_features_by_domain()
# Extract features
X = tsfel.time_series_features_extractor(cfg, data)
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