https://github.com/MaxBenChrist/tspreprocess

A Python package to preprocess time series
https://github.com/MaxBenChrist/tspreprocess

Last synced: about 1 month ago
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A Python package to preprocess time series

• Host: GitHub
• URL: https://github.com/MaxBenChrist/tspreprocess
• Owner: MaxBenChrist
• License: mit
• Created: 2017-07-20T14:34:00.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2017-11-01T20:56:42.000Z (over 6 years ago)
• Last Synced: 2024-02-10T05:44:00.146Z (5 months ago)
• Language: Python
• Homepage:
• Size: 23.4 KB
• Stars: 59
• Watchers: 15
• Forks: 10
• Open Issues: 12
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGES.rst
  • License: LICENSE.txt
  • Authors: AUTHORS.rst

Lists

• awesome_time_series_in_python - tspreprocess
• awesome_time_series_in_python - tspreprocess

README

        
**Disclaimer: This package is WIP. Do not take any APIs for granted.**

============

tspreprocess

============

Time series can contain noise, may be sampled under a non fitting rate or just need to be compressed.

*tspreprocess* is a library for such preprocessing tasks. It contains tools to transform and clean time series data for better analyses.

In detail, we are planning to add methods to do

* Denoising

* Compression

* Resampling

* ...

Our goal is to make this the most comprehensive time series preprocessing library.

Installation

============

Clone the repo, cd into it and install it with pip locally

.. code-block:: Python

    git clone https://github.com/MaxBenChrist/tspreprocess

    cd tspreprocess

    pip install -e .

You can run the test suite by

.. code-block:: Python

    python setup.py test

Relation to *tsfresh*

=====================

This package will based on the data formats from the python feature extraction pacakge *tsfresh*

(https://github.com/blue-yonder/tsfresh), allowing a seamless integration between both packages.