{"id":15008864,"url":"https://github.com/jrmontag/stldecompose","last_synced_at":"2025-04-05T13:08:55.571Z","repository":{"id":57471499,"uuid":"107179362","full_name":"jrmontag/STLDecompose","owner":"jrmontag","description":"A Python implementation of Seasonal and Trend decomposition using Loess (STL) for time series data.","archived":false,"fork":false,"pushed_at":"2019-03-03T04:39:56.000Z","size":302,"stargazers_count":185,"open_issues_count":7,"forks_count":51,"subscribers_count":10,"default_branch":"master","last_synced_at":"2025-04-05T13:08:51.424Z","etag":null,"topics":["modeling","python","python2","python3","statistics","time-series"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jrmontag.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-10-16T20:27:52.000Z","updated_at":"2025-03-17T02:48:50.000Z","dependencies_parsed_at":"2022-09-26T18:10:29.460Z","dependency_job_id":null,"html_url":"https://github.com/jrmontag/STLDecompose","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jrmontag%2FSTLDecompose","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jrmontag%2FSTLDecompose/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jrmontag%2FSTLDecompose/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jrmontag%2FSTLDecompose/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jrmontag","download_url":"https://codeload.github.com/jrmontag/STLDecompose/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247339158,"owners_count":20923014,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["modeling","python","python2","python3","statistics","time-series"],"created_at":"2024-09-24T19:21:10.970Z","updated_at":"2025-04-05T13:08:55.523Z","avatar_url":"https://github.com/jrmontag.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"STL 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 \u003chttp://www.statsmodels.org/stable/generated/statsmodels.tsa.seasonal.seasonal_decompose.html#statsmodels.tsa.seasonal.seasonal_decompose\u003e`_. In this implementation, the trend component is calculated by substituting a configurable `Loess regression \u003chttps://en.wikipedia.org/wiki/Local_regression\u003e`_ 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 \u003chttps://github.com/jrmontag/STLDecompose/blob/master/STL-usage-example.ipynb\u003e`_ 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 \u003chttp://www.statsmodels.org/stable/tsa.html\u003e`_ package\n- Hyndman's `OTexts reference on STL decomposition \u003chttps://www.otexts.org/fpp/6/5\u003e`_ \n- Cleveland et al. 1990 [`pdf \u003chttps://www.wessa.net/download/stl.pdf\u003e`_]\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjrmontag%2Fstldecompose","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjrmontag%2Fstldecompose","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjrmontag%2Fstldecompose/lists"}