https://github.com/sktime/tutorial_haicon_prologue_day
https://github.com/sktime/tutorial_haicon_prologue_day
Last synced: 5 months ago
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- Host: GitHub
- URL: https://github.com/sktime/tutorial_haicon_prologue_day
- Owner: sktime
- License: mit
- Created: 2025-05-25T17:12:32.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-06-01T11:33:57.000Z (8 months ago)
- Last Synced: 2025-06-01T20:18:27.163Z (8 months ago)
- Language: Jupyter Notebook
- Size: 8.1 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
Welcome to the sktime workshop at prologue day of HAICON 2025
============================================================
This 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.
This tutorial gives a walkthrough of forecasting and benchmarking forecasters with `sktime`
[sktime]: https://www.sktime.net
[](https://mybinder.org/v2/gh/sktime/tutorial_haicon_prologue_day/HEAD) [](https://discord.com/invite/54ACzaFsn7)
## :rocket: How to get started
In the tutorial, we will move through notebooks section by section.
You have different options how to run the tutorial notebooks:
* Run the notebooks in the cloud on [Binder] - for this you don't have to install anything!
* Run the notebooks on your machine. [Clone] this repository, and install all dependencies by pip install -r requirements.txt
## :bulb: Description
This tutorial is structured into four notebooks:
* Short introduction into sktime and how you can use sktime for various tasks (classification, anomaly detection, forecasting)
* Notebook focusing on advanced features for time series forecasting:
* Building pipelines in sktime to perform time series forecasting
* Using foundation models for forecasting in sktime.
* benchmarking forecasting algorithms in sktime
* outlook on upcoming benchmarking features and call for contributions
We invite anyone to get involved as a developer, user, supporter (or any combination of these).
## :movie_camera: Other Tutorials
- [Europython 2023 - General sktime introduction, half-day workshop](https://github.com/sktime/sktime-tutorial-europython-2023)
- [PyCon Prague 2023 - Forecasting, Advanced Pipelines, Benchmarking](https://github.com/sktime/sktime-tutorial-pydata-global-2023)
- [Pydata Amsterdam 2023 - Probabilistic prediction, forecasting, evaluation](https://github.com/sktime/sktime-tutorial-pydata-Amsterdam-2023)
- [ODSC Europe 2023 - Forecasting, Pipelines, and ML Engineering](https://github.com/sktime/sktime-tutorial-ODSC-Europe-2023/tree/main)
- [Pydata London 2023 - Time Series Classification, Regression, Distances & Kernels](https://github.com/sktime/sktime-tutorial-pydata-london-2023)
- [Pydata Berlin 2022 - Advanced Forecasting Tutorial](https://www.youtube.com/watch?v=4Rf9euAhjNc)
- [Pydata London 2022 - How to implement your own estimator in sktime](https://www.youtube.com/watch?v=S_3ewcvs_pg)
- [Pydata Global 2022 - Feature extraction, Pipelines, Tuning](https://github.com/sktime/sktime-tutorial-pydata-global-2022)
## :wave: How to contribute
If 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).
Any contributions are welcome, not just code!
## Installation instructions for local use
To run the notebooks locally, you will need:
* a local repository clone
* a python environment with required packages installed
### Cloning the repository
To clone the repository locally:
`git clone https://github.com/sktime/sktime-tutorial-pydata-global-2023`
### Using python venv
1. Create a python virtual environment:
`python -m venv sktime_pydata`
2. Activate your environment:
- `source sktime_pydata/bin/activate` for Linux
- sktime_pydata/Scripts/activate` for Windows
3. Install the requirements:
`pip install -r requirements`
4. If using jupyter: make the environment available in jupyter:
`python -m ipykernel install --user --name=sktime_pydata`