{"id":21022574,"url":"https://github.com/alro10/classical-time-series-forecasting","last_synced_at":"2026-05-21T04:05:27.380Z","repository":{"id":121032747,"uuid":"212430459","full_name":"Alro10/classical-time-series-forecasting","owner":"Alro10","description":"Coding from classical methods applying in time series forecasting","archived":false,"fork":false,"pushed_at":"2019-11-08T20:23:03.000Z","size":509,"stargazers_count":4,"open_issues_count":0,"forks_count":1,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-01-20T13:27:28.594Z","etag":null,"topics":["arima","arima-forecasting","python","sarimax","series-forecasting","time-series","time-series-forecasting","time-series-prediction"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Alro10.png","metadata":{"files":{"readme":"README.md","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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-10-02T20:02:09.000Z","updated_at":"2024-04-03T06:39:28.000Z","dependencies_parsed_at":null,"dependency_job_id":"ecdbed24-ed3e-48a7-9380-89c51771e4b2","html_url":"https://github.com/Alro10/classical-time-series-forecasting","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Alro10%2Fclassical-time-series-forecasting","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Alro10%2Fclassical-time-series-forecasting/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Alro10%2Fclassical-time-series-forecasting/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Alro10%2Fclassical-time-series-forecasting/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Alro10","download_url":"https://codeload.github.com/Alro10/classical-time-series-forecasting/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243456567,"owners_count":20293904,"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":["arima","arima-forecasting","python","sarimax","series-forecasting","time-series","time-series-forecasting","time-series-prediction"],"created_at":"2024-11-19T11:12:50.384Z","updated_at":"2026-05-21T04:05:27.326Z","avatar_url":"https://github.com/Alro10.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Classical Methods for Time Series Forecasting\nCoding, Python and R, from classical methods applying in time series forecasting\n\n## Resources\n\n- [An Introductory Study on Time Series Modeling and Forecasting](https://arxiv.org/pdf/1302.6613.pdf)\n\n- https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/\n\n- https://towardsdatascience.com/machine-learning-part-19-time-series-and-autoregressive-integrated-moving-average-model-arima-c1005347b0d7\n\n- https://towardsdatascience.com/econometric-approach-to-time-series-analysis-seasonal-arima-in-python-28f5782ee23\n\n## Methods\n\n- Moving Average\n- ARMA\n- ARIMA\n- [SARIMA](https://github.com/Alro10/classical-time-series-forecasting/blob/master/SARIMA.ipynb)\n  - The ```statsmodels``` implementation called this SARIMAX because the “X” addition to the method name means that the implementation also supports exogenous variables.\n\n## Packages \n\n- [Python-SARIMAX](https://www.statsmodels.org/stable/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html)\n- [R- Time series Analysis](http://db.ucsd.edu/static/TimeSeries.pdf)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falro10%2Fclassical-time-series-forecasting","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falro10%2Fclassical-time-series-forecasting","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falro10%2Fclassical-time-series-forecasting/lists"}