{"id":28429617,"url":"https://github.com/sktime/tutorial_haicon_prologue_day","last_synced_at":"2026-03-08T23:33:55.578Z","repository":{"id":296694154,"uuid":"990185331","full_name":"sktime/tutorial_haicon_prologue_day","owner":"sktime","description":null,"archived":false,"fork":false,"pushed_at":"2025-06-02T08:27:22.000Z","size":8498,"stargazers_count":0,"open_issues_count":1,"forks_count":1,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-08-21T20:19:17.303Z","etag":null,"topics":[],"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":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sktime.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,"zenodo":null}},"created_at":"2025-05-25T17:12:32.000Z","updated_at":"2025-06-02T08:27:25.000Z","dependencies_parsed_at":"2025-08-21T19:30:24.432Z","dependency_job_id":"e049e473-455c-4cf6-8daf-03e1da9cf9c4","html_url":"https://github.com/sktime/tutorial_haicon_prologue_day","commit_stats":null,"previous_names":["sktime/tutorial_haicon_prologue_day"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/sktime/tutorial_haicon_prologue_day","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Ftutorial_haicon_prologue_day","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Ftutorial_haicon_prologue_day/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Ftutorial_haicon_prologue_day/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Ftutorial_haicon_prologue_day/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sktime","download_url":"https://codeload.github.com/sktime/tutorial_haicon_prologue_day/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Ftutorial_haicon_prologue_day/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30277011,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-08T20:45:49.896Z","status":"ssl_error","status_checked_at":"2026-03-08T20:45:49.525Z","response_time":56,"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":[],"created_at":"2025-06-05T13:38:35.124Z","updated_at":"2026-03-08T23:33:55.538Z","avatar_url":"https://github.com/sktime.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"Welcome to the sktime workshop at prologue day of HAICON 2025\n============================================================\n\nThis tutorial is about [sktime] - a unified framework for machine learning with time series. sktime contains algorithms and tools for building, applying, evaluating modular pipelines and composites for a variety of time series learning tasks, including forecasting, classification, regression.\n\nThis tutorial gives a walkthrough of forecasting and benchmarking forecasters with `sktime` \n\n[sktime]: https://www.sktime.net\n\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/sktime/tutorial_haicon_prologue_day/HEAD) [![!discord](https://img.shields.io/static/v1?logo=discord\u0026label=discord\u0026message=chat\u0026color=lightgreen)](https://discord.com/invite/54ACzaFsn7) \n\n## :rocket: How to get started\n\nIn the tutorial, we will move through notebooks section by section.\n\nYou have different options how to run the tutorial notebooks:\n\n* Run the notebooks in the cloud on [Binder] - for this you don't have to install anything!\n* Run the notebooks on your machine. [Clone] this repository, and install all dependencies by pip install -r requirements.txt\n\n## :bulb: Description\n\nThis tutorial is structured into four notebooks:\n* Short introduction into sktime and how you can use sktime for various tasks (classification, anomaly detection, forecasting)\n* Notebook focusing on advanced features for time series forecasting:\n  * Building pipelines in sktime to perform time series forecasting\n  * Using foundation models for forecasting in sktime. \n* benchmarking forecasting algorithms in sktime\n* outlook on upcoming benchmarking features and call for contributions\n\nWe invite anyone to get involved as a developer, user, supporter (or any combination of these).\n\n\n## :movie_camera: Other Tutorials\n\n- [Europython 2023 - General sktime introduction, half-day workshop](https://github.com/sktime/sktime-tutorial-europython-2023)\n\n- [PyCon Prague 2023 - Forecasting, Advanced Pipelines, Benchmarking](https://github.com/sktime/sktime-tutorial-pydata-global-2023)\n\n- [Pydata Amsterdam 2023 - Probabilistic prediction, forecasting, evaluation](https://github.com/sktime/sktime-tutorial-pydata-Amsterdam-2023)\n\n- [ODSC Europe 2023 - Forecasting, Pipelines, and ML Engineering](https://github.com/sktime/sktime-tutorial-ODSC-Europe-2023/tree/main)\n\n- [Pydata London 2023 - Time Series Classification, Regression, Distances \u0026 Kernels](https://github.com/sktime/sktime-tutorial-pydata-london-2023)\n\n- [Pydata Berlin 2022 - Advanced Forecasting Tutorial](https://www.youtube.com/watch?v=4Rf9euAhjNc)\n\n- [Pydata London 2022 - How to implement your own estimator in sktime](https://www.youtube.com/watch?v=S_3ewcvs_pg)\n\n- [Pydata Global 2022 - Feature extraction, Pipelines, Tuning](https://github.com/sktime/sktime-tutorial-pydata-global-2022)\n\n\n## :wave: How to contribute\n\nIf you're interested in contributing to sktime, you can find out more how to get involved [here](https://www.sktime.net/en/latest/get_involved.html).\n\nAny contributions are welcome, not just code!\n\n## Installation instructions for local use\n\nTo run the notebooks locally, you will need:\n\n* a local repository clone\n* a python environment with required packages installed\n\n### Cloning the repository\n\nTo clone the repository locally:\n\n`git clone https://github.com/sktime/sktime-tutorial-pydata-global-2023`\n\n### Using python venv\n\n1. 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