{"id":24714238,"url":"https://github.com/avisionh/analysis-structuralbreak","last_synced_at":"2026-02-07T08:01:35.557Z","repository":{"id":40167323,"uuid":"247651453","full_name":"avisionh/analysis-structuralbreak","owner":"avisionh","description":"Practical introduction to modelling and testing for structural breaks in time-series data.","archived":false,"fork":false,"pushed_at":"2022-12-08T04:02:29.000Z","size":2167,"stargazers_count":2,"open_issues_count":10,"forks_count":1,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-08-01T14:48:35.439Z","etag":null,"topics":["chow-test","compositional-data","stl","structural-break-test","time-series"],"latest_commit_sha":null,"homepage":"https://avisionh.github.io/analysis-structuralbreak","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/avisionh.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-03-16T08:42:56.000Z","updated_at":"2021-01-13T06:39:42.000Z","dependencies_parsed_at":"2023-01-25T07:31:19.007Z","dependency_job_id":null,"html_url":"https://github.com/avisionh/analysis-structuralbreak","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/avisionh/analysis-structuralbreak","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/avisionh%2Fanalysis-structuralbreak","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/avisionh%2Fanalysis-structuralbreak/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/avisionh%2Fanalysis-structuralbreak/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/avisionh%2Fanalysis-structuralbreak/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/avisionh","download_url":"https://codeload.github.com/avisionh/analysis-structuralbreak/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/avisionh%2Fanalysis-structuralbreak/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29189675,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-07T07:37:03.739Z","status":"ssl_error","status_checked_at":"2026-02-07T07:37:03.029Z","response_time":63,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["chow-test","compositional-data","stl","structural-break-test","time-series"],"created_at":"2025-01-27T08:16:06.344Z","updated_at":"2026-02-07T08:01:35.531Z","avatar_url":"https://github.com/avisionh.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Training: Time-Series\nWelcome to this repository of training materials for analysing time-series data.\n\nThe training materials will be written in [Python](https://www.python.org/) and hosted via [Jupyter Book](https://jupyterbook.org/intro.html).\n\n## Who are these training materials for?\nThese training materials are designed with data practitioners in mind.\n\nIn particular, it is created from this perspective to empower readers to immediately begin modelling and forecasting time-series data.\n\n## How are the training materials organised?\nThe book will introduce some standard time-series theory briefly and focus on the practical introduction of analysing and modelling it.\n\nIt particular, it will cover topics such as:\n- [ ] Compositional data\n- [ ] Stationarity\n- [ ] ARIMA modelling","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Favisionh%2Fanalysis-structuralbreak","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Favisionh%2Fanalysis-structuralbreak","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Favisionh%2Fanalysis-structuralbreak/lists"}