{ "info": { "author": "Shay Palachy", "author_email": "shay.palachy@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Programming Language :: Python", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Software Development :: Libraries", "Topic :: Software Development :: Libraries :: Python Modules" ], "description": "stationarizer \u0df4\n###############\n\n|PyPI-Status| |PyPI-Versions| |Build-Status| |Codecov| |Codacy| |Requirements| |LICENCE|\n\nSmart, automatic detection and stationarization of non-stationary time series data.\n\n.. code-block:: python\n\n >>> from stationarizer import simple_auto_stationarize\n >>> simple_auto_stationarize(my_dataframe)\n\n.. contents::\n\n.. section-numbering::\n\n\nInstallation\n============\n\n.. code-block:: bash\n\n pip install stationarizer\n\n\nFeatures\n========\n\n* Plays nice with ``pandas.DataFrame`` inputs.\n* Pure python.\n* Supports Python 3.6+.\n\n\nUse\n===\n\nSimple auto-stationarization\n----------------------------\n\nThe only stationarization pipeline implemented is ``simple_auto_stationarize``, which can be called with:\n\n.. code-block:: python\n\n >>> from stationarizer import simple_auto_stationarize\n >>> stationarized_df = simple_auto_stationarize(my_dataframe)\n\n\nThe 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).\n\n\nMethodology\n===========\n\nSimple auto-stationarization\n----------------------------\n\nCurrently only the following simple flow - dealing with unit roots - is implemented:\n\n* 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).\n* Both the Augmented Dickey-Fuller unit root test and the KPSS test are performed for each of the series.\n* The p-values of all tests are corrected to control the false discovery rate (FDR) at some given level, using the Benjamini\u2013Yekutieli procedure.\n* The joint ADF-KPSS results are interpreted for each test.\n* For each time series for which the presence of a unit root cannot be rejected, the series is diffentiated.\n* For each time series for which the presence of a trend cannot be rejected, the series is de-trended.\n* If any series was diffrentiated, then any un-diffrentiated time series (if any) are trimmed by one step to match the resulting series length.\n\n\nContributing\n============\n\nPackage 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.\n\nInstalling for development\n----------------------------\n\nClone:\n\n.. code-block:: bash\n\n git clone git@github.com:shaypal5/stationarizer.git\n\n\nInstall in development mode, including test dependencies:\n\n.. code-block:: bash\n\n cd stationarizer\n pip install -e '.[test]'\n\n\nTo also install ``fasttext``, see instructions in the Installation section.\n\n\nRunning the tests\n-----------------\n\nTo run the tests use:\n\n.. code-block:: bash\n\n cd stationarizer\n pytest\n\n\nAdding documentation\n--------------------\n\nThe 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. 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