This tutorial presents sktime - a unified, open source framework for machine learning with time series in python. sktime provides interfaces to algorithms of various types, and modular tools for pipelining, composition, and tuning. You will learn how identify your learning task, and how to build, use, and evaluate different algorithms on real-world data sets.

All tutorial notebooks are available in this repository and runnable from the cloud: https://github.com/sktime/sktime-tutorial-europython-2023

Time series are ubiquitous in real-world applications, but often add considerable complications to data science workflows.

In this tutorial, we will present 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. In addition, 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.

To solve these problems, sktime provides various time series algorithms and modular tools for pipelining, composition and tuning. In addition, sktime is interoperable with common libraries in the data science stack, including scikit-learn, statsmodels and prophet.

You will learn how to use, combine, tune and evaluate different algorithms on real-world data sets. We'll work through all of this step by step using Jupyter Notebooks.

sktime is an openly governed open source community where everyone can join or become a leader. We encourage contributions and provide mentoring opportunities.