https://github.com/jrmontag/stldecompose

A Python implementation of Seasonal and Trend decomposition using Loess (STL) for time series data.
https://github.com/jrmontag/stldecompose

modeling python python2 python3 statistics time-series

Last synced: 2 days ago
JSON representation

A Python implementation of Seasonal and Trend decomposition using Loess (STL) for time series data.

• Host: GitHub
• URL: https://github.com/jrmontag/stldecompose
• Owner: jrmontag
• License: mit
• Created: 2017-10-16T20:27:52.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2019-03-03T04:39:56.000Z (over 5 years ago)
• Last Synced: 2024-10-01T19:53:49.343Z (2 days ago)
• Topics: modeling, python, python2, python3, statistics, time-series
• Language: Jupyter Notebook
• Homepage:
• Size: 295 KB
• Stars: 180
• Watchers: 10
• Forks: 49
• Open Issues: 7
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

Awesome Lists containing this project

README

        
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 `_. 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. 

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 `_ 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 [email protected]:jrmontag/STLDecompose.git 

    (env) $ cd STLDecompose; pip install . 

More Resources

--------------

- ``statsmodels`` `Time Series analysis `_ package

- Hyndman's `OTexts reference on STL decomposition `_ 

- Cleveland et al. 1990 [`pdf `_]