{"id":28429597,"url":"https://github.com/sktime/sktime-tutorial-europython-2023","last_synced_at":"2025-07-10T22:39:42.815Z","repository":{"id":181628241,"uuid":"664728926","full_name":"sktime/sktime-tutorial-europython-2023","owner":"sktime","description":null,"archived":false,"fork":false,"pushed_at":"2023-09-08T09:27:36.000Z","size":11816,"stargazers_count":14,"open_issues_count":3,"forks_count":2,"subscribers_count":4,"default_branch":"main","last_synced_at":"2025-06-12T03:21:36.052Z","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":"2023-07-10T16:01:29.000Z","updated_at":"2025-05-12T13:46:38.000Z","dependencies_parsed_at":"2024-03-14T15:24:49.873Z","dependency_job_id":"bbb380f6-16d8-4bbe-984f-f1afa6927058","html_url":"https://github.com/sktime/sktime-tutorial-europython-2023","commit_stats":{"total_commits":31,"total_committers":3,"mean_commits":"10.333333333333334","dds":0.06451612903225812,"last_synced_commit":"111677ab898cce36085fd4bc3a000cae95e2112b"},"previous_names":["sktime/sktime-tutorial-europython-2023"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/sktime/sktime-tutorial-europython-2023","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fsktime-tutorial-europython-2023","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fsktime-tutorial-europython-2023/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fsktime-tutorial-europython-2023/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fsktime-tutorial-europython-2023/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sktime","download_url":"https://codeload.github.com/sktime/sktime-tutorial-europython-2023/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fsktime-tutorial-europython-2023/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":260166668,"owners_count":22968689,"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":"2025-06-05T13:38:31.384Z","updated_at":"2025-07-04T18:30:26.985Z","avatar_url":"https://github.com/sktime.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"![](images/team.jpg)\n\nWelcome to the sktime tutorial at Europython 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 is an introductory `sktime` half-day tutorial with:\n\n* a general introduction to `sktime`\n* forecasting with `sktime` - uni/multivariate, hierarchical/global, probabilistic\n* feature extraction, transformation pipelines, parameter tuning, autoML\n* time series classification, regression, and clustering with `sktime`\n* customizing time series distances, kernels, time series aligners and alignment distances\n* engineering topics:\n    * APIs, estimator and dependency management\n    * writing `sktime` compatible 3rd party estimators\n* deploying `sktime` in production using `mlflow` with the `mlflavours` plugin\n\n[sktime]: https://sktime.net\n\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/sktime/sktime-tutorial-europython-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-europython-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\nThis tutorial presents [sktime] - a unified framework for machine learning with time series. sktime covers multiple time series learning problems, including time series transformation, classification and forecasting, among others.`sktime` allows you to easily apply an algorithm for one task to solve another (e.g. a scikit-learn regressor to solve a forecasting problem). In the tutorial, you will learn about how you can identify these problems, what their key differences are and how they are related.\n\n`sktime` provides various time series algorithms and modular composition tools for pipelining, ensembling and tuning.\n`sktime` also provides API compatible interfaces to many popular libraries, such as `statsmodels`, `prophet`, `statsforecast`, `tslearn`, `tsfresh`, etc,\nwhich can be readily combined using `sktime` composition patterns.\n\nIn this tutorial, you will learn how to use, combine, tune and evaluate different algorithms on real-world data sets.\nThe tutorial consists of step-by-step using Jupyter Notebooks.\n\n`sktime` not just a package, but also an active community which aims to be welcoming to new joiners.\nWe invite anyone to get involved as a developer, user, supporter (or any combination of these).\n\n## :movie_camera: Other Tutorials:\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- [Pydata London 2023 - Time Series Classification, Regression, Distances \u0026 Kernels](https://github.com/sktime/sktime-tutorial-pydata-london-2023)\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\nWe also invite everyone to the \"getting started with contributions\" onboarding feature at the community sprint at EuroPython (July 22-23)!\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-europython-2023.git`\n\n### Using conda env\n\n1. Create a python virtual environment:\n`conda create -y -n europython_sktime python=3.9`\n2. Install required packages:\n`conda install -y -n europython_sktime pip sktime seaborn jupyter pmdarima statsmodels dtw-python`\n3. Activate your environment:\n`conda activate europython_sktime`\n4. If using jupyter: make the environment available in jupyter:\n`python -m ipykernel install --user --name=europython_sktime`\n\n### Using python venv\n\n1. Create a python virtual environment:\n`python -m venv europython_sktime`\n2. Activate your environment:\n`source europython_sktime/bin/activate`\n3. Install the requirements:\n`pip install sktime seaborn jupyter pmdarima statsmodels dtw-python`\n4. If using jupyter: make the environment available in jupyter:\n`python -m ipykernel install --user --name=europython_sktime`\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsktime%2Fsktime-tutorial-europython-2023","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsktime%2Fsktime-tutorial-europython-2023","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsktime%2Fsktime-tutorial-europython-2023/lists"}