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
Sessions
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. sktime has a rich framework for building pipelines across multiple learning tasks that it supports, including forecasting, time series classification, regression, clustering. This tutorial explains basic and advanced sktime pipeline constructs, and introduces in detail the time series transformer which is the main component in all types of pipelines. It is a continuation of the sktime introductory tutorial at pydata global 2021.