Skip to content
Smart, automatic detection and stationarization of non-stationary time series data.
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
notebooks
stationarizer
tests
.codecov.yml skeleton commit Mar 11, 2019
.coveragerc
.gitattributes
.gitignore
.travis.yml
LICENSE
MANIFEST.in
Pipfile
Pipfile.lock nan issue solved Jun 19, 2019
README.rst
mit_license_badge.svg
pytest.ini
setup.cfg
setup.py
versioneer.py

README.rst

stationarizer ෴

PyPI-Status PyPI-Versions Build-Status Codecov Codacy Badge Requirements Status LICENCE

Smart, automatic detection and stationarization of non-stationary time series data.

>>> from stationarizer import simple_auto_stationarize
>>> simple_auto_stationarize(my_dataframe)

1   Installation

pip install stationarizer

2   Features

  • Plays nice with pandas.DataFrame inputs.
  • Pure python.
  • Supports Python 3.6+.

3   Use

3.1   Simple auto-stationarization

The only stationarization pipeline implemented is simple_auto_stationarize, which can be called with:

>>> from stationarizer import simple_auto_stationarize
>>> stationarized_df = simple_auto_stationarize(my_dataframe)

The level to which false discovery rate (FDR) is controled can be configured with the alpha parameter, while the method for multitest error control can be configured with multitest (changing this can change alpha to control for FWER instead).

4   Methodology

4.1   Simple auto-stationarization

Currently only the following simple flow - dealing with unit roots - is implemented:

  • Data validation is performed: all columns are checked to be numeric, and the time dimension is assumed to be larger than the number of series (although this is not mandatory, and so only a warning is thrown in case of violation).
  • Both the Augmented Dickey-Fuller unit root test and the KPSS test are performed for each of the series.
  • The p-values of all tests are corrected to control the false discovery rate (FDR) at some given level, using the Benjamini–Yekutieli procedure.
  • The joint ADF-KPSS results are interpreted for each test.
  • For each time series for which the presence of a unit root cannot be rejected, the series is diffentiated.
  • For each time series for which the presence of a trend cannot be rejected, the series is de-trended.
  • If any series was diffrentiated, then any un-diffrentiated time series (if any) are trimmed by one step to match the resulting series length.

5   Contributing

Package author and current maintainer is Shay Palachy (shay.palachy@gmail.com); You are more than welcome to approach him for help. Contributions are very welcomed.

5.1   Installing for development

Clone:

git clone git@github.com:shaypal5/stationarizer.git

Install in development mode, including test dependencies:

cd stationarizer
pip install -e '.[test]'

To also install fasttext, see instructions in the Installation section.

5.2   Running the tests

To run the tests use:

cd stationarizer
pytest

5.3   Adding documentation

The project is documented using the numpy docstring conventions, which were chosen as they are perhaps the most widely-spread conventions that are both supported by common tools such as Sphinx and result in human-readable docstrings. When documenting code you add to this project, follow these conventions.

Additionally, if you update this README.rst file, use python setup.py checkdocs to validate it compiles.

6   Credits

Created by Shay Palachy (shay.palachy@gmail.com).

You can’t perform that action at this time.