{ "info": { "author": "Josh Montague", "author_email": "joshua.montague@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "License :: OSI Approved :: MIT License", "Programming Language :: Python", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: Implementation :: CPython", "Programming Language :: Python :: Implementation :: PyPy", "Topic :: Scientific/Engineering" ], "description": "\nSTL Decompose\n=============\n\nThis is a relatively naive Python implementation of a seasonal and trend decomposition using Loess smoothing. Commonly referred to as an \"STL decomposition\", Cleveland's 1990 paper is the canonical reference. \n\nThis implementation is a variation of (and takes inspiration from) the implementation of the ``seasonal_decompose`` method `in statsmodels `_. In this implementation, the trend component is calculated by substituting a configurable `Loess regression `_ for the convolutional method used in ``seasonal_decompose``. It also extends the existing ``DecomposeResult`` from ``statsmodels`` to allow for forecasting based on the calculated decomposition. \n\n\nUsage\n-----\n\nThe ``stldecompose`` package is relatively lightweight. It uses ``pandas.Dataframe`` for inputs and outputs, and exposes only a couple of primary methods - ``decompose()`` and ``forecast()`` - as well as a handful of built-in forecasting functions. \n\nSee `the included IPython notebook `_ for more details and usage examples.\n\n\nInstallation\n------------\n\nA Python 3 virtual environment is recommended.\n\nThe preferred method of installation is via ``pip``::\n\n (env) $ pip install stldecompose\n\nIf you'd like the bleeding-edge version, you can also install from this Github repo::\n\n (env) $ git clone git@github.com:jrmontag/STLDecompose.git \n (env) $ cd STLDecompose; pip install . \n\n\nMore Resources\n--------------\n\n- ``statsmodels`` `Time Series analysis `_ package\n- Hyndman's `OTexts reference on STL decomposition `_ \n- Cleveland et al. 1990 [`pdf `_]\n\n\n", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": 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