{"id":28429607,"url":"https://github.com/sktime/sktime-tutorial-pydata-global-2023","last_synced_at":"2025-07-04T18:30:22.233Z","repository":{"id":211089152,"uuid":"725275707","full_name":"sktime/sktime-tutorial-pydata-global-2023","owner":"sktime","description":null,"archived":false,"fork":false,"pushed_at":"2023-12-09T08:37:55.000Z","size":6885,"stargazers_count":17,"open_issues_count":0,"forks_count":5,"subscribers_count":5,"default_branch":"main","last_synced_at":"2025-06-12T02:38:57.961Z","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":"bsd-3-clause","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}},"created_at":"2023-11-29T20:02:27.000Z","updated_at":"2024-06-17T16:18:48.000Z","dependencies_parsed_at":null,"dependency_job_id":"8d58d56e-83a2-40f2-9903-7c113dccbf16","html_url":"https://github.com/sktime/sktime-tutorial-pydata-global-2023","commit_stats":{"total_commits":22,"total_committers":4,"mean_commits":5.5,"dds":0.6363636363636364,"last_synced_commit":"eabc4071827e6de9db5007d98c15bbc63648b1ef"},"previous_names":["sktime/sktime-tutorial-pydata-global-2023"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/sktime/sktime-tutorial-pydata-global-2023","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fsktime-tutorial-pydata-global-2023","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fsktime-tutorial-pydata-global-2023/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fsktime-tutorial-pydata-global-2023/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fsktime-tutorial-pydata-global-2023/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sktime","download_url":"https://codeload.github.com/sktime/sktime-tutorial-pydata-global-2023/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fsktime-tutorial-pydata-global-2023/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261033740,"owners_count":23100372,"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":[],"created_at":"2025-06-05T13:38:32.752Z","updated_at":"2025-07-04T18:30:22.227Z","avatar_url":"https://github.com/sktime.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"Welcome to the sktime workshop at pydata global 2023\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\n`sktime` is easily extensible by anyone, and interoperable with the python data science stack.\n\nThis tutorial gives a walkthrough of new `sktime` features in 2022-2023, together with an updated general introduction.\n\n[sktime]: https://www.sktime.net\n\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/sktime/sktime-tutorial-pydata-global-2023/main?filepath=notebooks) [![!discord](https://img.shields.io/static/v1?logo=discord\u0026label=discord\u0026message=chat\u0026color=lightgreen)](https://discord.com/invite/54ACzaFsn7) [![!slack](https://img.shields.io/static/v1?logo=linkedin\u0026label=LinkedIn\u0026message=news\u0026color=lightblue)](https://www.linkedin.com/company/scikit-time/)\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, get [conda], install the required packages (`sktime`, `seaborn`, `jupyter`) in an environment, and open the notebooks with that environment. For detail instructions, see below. For troubleshooting, see sktime's more detailed [installation instructions].\n* or, use python venv, and/or an editable install of this repo as a package. Instructions below.\n\n[Binder]: https://mybinder.org/v2/gh/sktime/sktime-tutorial-pydata-global-2023/main?filepath=notebooks\n[clone]: https://help.github.com/en/github/creating-cloning-and-archiving-repositories/cloning-a-repository\n[conda]: https://docs.conda.io/en/latest/\n[installation instructions]: https://www.sktime.net/en/latest/installation.html\n\nPlease let us know on the [sktime discord](https://discord.com/invite/54ACzaFsn7) if you have any issues during the conference, or join to ask for help anytime.\n\n## :bulb: Description\n\nThe tutorial gives an updated 30 minute introduction to sktime base features with a focus on forecasting, and then proceeds with a vignette slideshow introducing the most important features added since pydata global 2022:\n\n* Upgraded base interface using scikit-base\n* Rework of the forecasting pipeline interface\n* fully distributional probabilistic forecasts and metrics\n* extended parallelism, including parallel broadcasting to hierarchical data\n* composable time series classifiers, regressors, distances, time series aligners\n* reproducibility features such as blueprint and fitted estimator serialization\n* benchmarking frameworks for replicating studies such as M4/M5\n\nEach feature vignette will come with links to further, extended tutorials where applicable.\n\n`sktime` not just a package, but also an active community which aims to be welcoming to new joiners.\n\nsktime is developed by an open community, with aims of ecosystem integration in a neutral, charitable space. We welcome contributions and seek to provides opportunity for anyone worldwide.\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 conda env\n\n1. Create a python virtual environment:\n`conda create -y -n sktime_pydata python=3.11`\n2. Install required packages:\n`conda install -y -n sktime_pydata pip sktime seaborn jupyter pmdarima statsmodels dtw-python`\n3. Activate your environment:\n`conda activate sktime_pydata`\n4. If using jupyter: make the environment available in jupyter:\n`python -m ipykernel install --user --name=sktime_pydata`\n\n### Using python venv\n\n1. Create a python virtual environment:\n`python -m venv sktime_pydata`\n2. Activate your environment:\n - `source sktime_pydata/bin/activate` for Linux\n - sktime_pydata/Scripts/activate` for Windows\n3. Install the requirements:\n`pip install -r requirements`\n4. If using jupyter: make the environment available in jupyter:\n`python -m ipykernel install --user --name=sktime_pydata`\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsktime%2Fsktime-tutorial-pydata-global-2023","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsktime%2Fsktime-tutorial-pydata-global-2023","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsktime%2Fsktime-tutorial-pydata-global-2023/lists"}