Metadata-Version: 2.1
Name: tsfel
Version: 0.1.2
Summary: Library for time series feature extraction
Home-page: https://github.com/fraunhoferportugal/tsfel/
Author: Fraunhofer Portugal
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: gspread (>=3.1.0)
Requires-Dist: matplotlib (>=3.1.0)
Requires-Dist: numpy (>=1.17.4)
Requires-Dist: oauth2client (>=4.1.3)
Requires-Dist: pandas (>=0.24.2)
Requires-Dist: scipy (>=1.4.0)
Requires-Dist: setuptools (>=41.0.1)

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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)
```

## 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.


