semvec/pypi-packages huggingface dataset:
stldecompose 0.0.5
A Python implementation of seasonal trend with Loess (STL) time series decomposition
License: MIT


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>`_]