tsfresh extracts relevant characteristics from time series
This repository contains the TSFRESH python package. The abbreviation stands for
"Time Series Feature extraction based on scalable hypothesis tests".
The package contains many feature extraction methods and a robust feature selection algorithm.
Spend less time on feature engineering
Data Scientists often spend most of their time either cleaning data or building features. While we cannot change the first thing, the second can be automated. TSFRESH frees your time spent on building features by extracting them automatically. Hence, you have more time to study the newest deep learning paper, read hacker news or build better models.
Automatic extraction of 100s of features
TSFRESH automatically extracts 100s of features from time series. Those features describe basic characteristics of the time series such as the number of peaks, the average or maximal value or more complex features such as the time reversal symmetry statistic.
The set of features can then be used to construct statistical or machine learning models on the time series to be used for example in regression or classification tasks.
Forget irrelevant features
Time series often contain noise, redundancies or irrelevant information. As a result most of the extracted features will not be useful for the machine learning task at hand.
To avoid extracting irrelevant features, the TSFRESH package has a built-in filtering procedure. This filtering procedure evaluates the explaining power and importance of each characteristic for the regression or classification tasks at hand.
It is based on the well developed theory of hypothesis testing and uses a multiple test procedure. As a result the filtering process mathematically controls the percentage of irrelevant extracted features.
The TSFRESH package is described in the following open access paper
- Christ, M., Braun, N., Neuffer, J. and Kempa-Liehr A.W. (2018). Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh -- A Python package). Neurocomputing 307 (2018) 72-77, doi:10.1016/j.neucom.2018.03.067.
The FRESH algorithm is described in the following whitepaper
- Christ, M., Kempa-Liehr, A.W. and Feindt, M. (2017). Distributed and parallel time series feature extraction for industrial big data applications. ArXiv e-print 1610.07717, https://arxiv.org/abs/1610.07717.
Advantages of tsfresh
TSFRESH has several selling points, for example
- it is field tested
- it is unit tested
- the filtering process is statistically/mathematically correct
- it has a comprehensive documentation
- it is compatible with sklearn, pandas and numpy
- it allows anyone to easily add their favorite features
- it both runs on your local machine or even on a cluster
If you are interested in the technical workings, go to see our comprehensive Read-The-Docs documentation at http://tsfresh.readthedocs.io.
The algorithm, especially the filtering part are also described in the paper mentioned above.
If you have some questions or feedback you can find the developers in the gitter chatroom.
We appreciate any contributions, if you are interested in helping us to make TSFRESH the biggest archive of feature extraction methods in python, just head over to our How-To-Contribute instructions.
If you want to try out
tsfresh quickly or if you want to integrate it into your workflow, we also have a docker image available:
docker pull nbraun/tsfresh
The research and development of TSFRESH was funded in part by the German Federal Ministry of Education and Research under grant number 01IS14004 (project iPRODICT).
Release history Release notifications | RSS feed
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size tsfresh-0.16.0-py2.py3-none-any.whl (85.1 kB)||File type Wheel||Python version py2.py3||Upload date||Hashes View|
|Filename, size tsfresh-0.16.0.tar.gz (3.4 MB)||File type Source||Python version None||Upload date||Hashes View|
Hashes for tsfresh-0.16.0-py2.py3-none-any.whl