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

🚀 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

💡 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).

🎥 Other Tutorials

• Europython 2023 - General sktime introduction, half-day workshop

• PyCon Prague 2023 - Forecasting, Advanced Pipelines, Benchmarking

• Pydata Amsterdam 2023 - Probabilistic prediction, forecasting, evaluation

• ODSC Europe 2023 - Forecasting, Pipelines, and ML Engineering

• Pydata London 2023 - Time Series Classification, Regression, Distances & Kernels

• Pydata Berlin 2022 - Advanced Forecasting Tutorial

• Pydata London 2022 - How to implement your own estimator in sktime

• Pydata Global 2022 - Feature extraction, Pipelines, Tuning

👋 How to contribute

If you're interested in contributing to sktime, you can find out more how to get involved here.

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