Metadata-Version: 1.1
Name: stldecompose
Version: 0.0.5
Summary: A Python implementation of seasonal trend with Loess (STL) time series decomposition
Home-page: https://github.com/jrmontag/STLDecompose
Author: Josh Montague
Author-email: joshua.montague@gmail.com
License: MIT
Description: 
        STL Decompose
        =============
        
        This 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.  
        
        This implementation is a variation of (and takes inspiration from) the 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. 
        
        
        Usage
        -----
        
        The ``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. 
        
        See `the included IPython notebook <https://github.com/jrmontag/STLDecompose/blob/master/STL-usage-example.ipynb>`_ for more details and usage examples.
        
        
        Installation
        ------------
        
        A Python 3 virtual environment is recommended.
        
        The preferred method of installation is via ``pip``::
        
            (env) $ pip install stldecompose
        
        If you'd like the bleeding-edge version, you can also install from this Github repo::
         
            (env) $ git clone git@github.com:jrmontag/STLDecompose.git 
            (env) $ cd STLDecompose; pip install . 
        
        
        More Resources
        --------------
        
        - ``statsmodels`` `Time Series analysis <http://www.statsmodels.org/stable/tsa.html>`_ package
        - Hyndman's `OTexts reference on STL decomposition <https://www.otexts.org/fpp/6/5>`_ 
        - Cleveland et al. 1990 [`pdf <https://www.wessa.net/download/stl.pdf>`_]
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Scientific/Engineering
