sktime - python toolbox for time series – introduction and new features 2024: foundation models, deep learning backends, probabilistic models, hierarchical demand forecasting, marketplace features
Franz Kiraly, Felipe Angelim, Muhammad Armaghan Shakir, Benedikt Heidrich
sktime is the most widely used scikit-learn compatible framework library for learning with time series. sktime is maintained by a neutral non-profit under permissive license, easily extensible by anyone, and interoperable with the python data science stack.
This tutorial gives a hands-on introduction to sktime, for common time series learning tasks such as forecasting, starting with a general overview of the package and forecasting interfaces for uni- and multivariate forecasts with endo-/exogeneous data, probabilistic forecasts, and forecasting in the presence of hierarchical data.
The tutorial then proceeds to showcase some of the newest features in 2024, based on a hierarchical demand forecasting use case example: support for foundation models, hugging face connectors, advanced support for hierarchical and global forecasts, and integration features for creating API compatible algorithms and sharing them via the sktime discoverability tools.
Machine and Deep Learning
Room 5