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        "author": "Goh Kun Shun",
        "author_email": "gohkunshun@airasia.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"
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        "description": "\nSTL Decompose\n=============\n\nThis is a relatively naive Python implementation of the \"Seasonal and Trend decomposition using Loess\" time series decomposition (\"STL decomposition,\" Cleveland et al. 1990 [`pdf <https://www.wessa.net/download/stl.pdf>`_]).  \n\nThis implementation is a variation of (and takes inspiration from) the current implementation of the ``seasonal_decompose`` method `in statsmodels <http://www.statsmodels.org/stable/generated/statsmodels.tsa.seasonal.seasonal_decompose.html#statsmodels.tsa.seasonal.seasonal_decompose>`_. In this implementation, the trend component is calculated by substituting a configurable `Loess regression <https://en.wikipedia.org/wiki/Local_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 <https://github.com/jrmontag/STLDecompose/blob/master/STL%20usage%20example.ipynb>`_ 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 <http://www.statsmodels.org/stable/tsa.html>`_ package\n- Hyndman's `OTexts reference on STL decomposition <https://www.otexts.org/fpp/6/5>`_ \n\n\n",
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        "license": "MIT",
        "maintainer": "",
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        "name": "stldecomposemod",
        "package_url": "https://pypi.org/project/stldecomposemod/",
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        "summary": "A Python implementation of seasonal trend with Loess (STL) time series decomposition MODIFIED",
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