Slides & Code

This tutorial will make use of the aeon toolkit for its code examples. aeon is an open source, scikit-learn compatible framework for time series machine learning. The latest advances in the fields of time series classification and regression are all available through aeon. aeon does more than classification and regression, with modules for clustering, anomaly detection, segmentation and more.

The code examples will be provided in Jupyter notebooks, which will be available for each topic. All the notebooks and slides are available on this GitHub repository. A slide and notebook file is available for each tutorial section, split up into 10 parts.

Links to individual slides and notebooks are provided on the schedule page, as well as links to the Google Colab versions of the notebooks. We also provide the links below:

The following will launch the notebooks on binder. A view-only version is available via nbviewer.

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