PyData Global 2023

Benedikt Heidrich

I completed a Master of Science degree in informatics in 2019 with the Karlsruhe Institute of Technology. I am working towards a PhD in Informatics at the Karlsruhe Institute of Technology. My research focuses on using deep generative models in energy systems and coping with concept drift in energy time series forecasting. Additionally, I investigate how general pipeline architecture has to be designed for time series analysis tasks

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Sessions

12-06
13:00
90min
sktime - python toolbox for time series: new features 2023 – advanced pipelines, probabilistic forecasting, parallelism support, composable classifiers and distances, reproducibility features
sktime community, Benedikt Heidrich, Anirban Ray, Franz Kiraly

sktime is a widely used scikit-learn compatible library for learning with time series. sktime is easily extensible by anyone, and interoperable with the pydata/numfocus stack.

This tutorial gives an updated introduction to sktime and presents a vignette slideshow with the most important features added since pydata global 2022.

Machine Learning Track
Machine Learning Track