PyData Global 2023

sktime - python toolbox for time series: new features 2023 – advanced pipelines, probabilistic forecasting, parallelism support, composable classifiers and distances, reproducibility features
12-06, 13:00–14:30 (UTC), Machine Learning Track

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.


The tutorial will give an updated 30 minute introduction to sktime base features with a focus on forecasting, and then proceed with a vignette slideshow introducing the most important features added since pydata global 2022:

  • Upgraded base interface using scikit-base
  • Rework of the forecasting pipeline interface
  • fully distributional probabilistic forecasts and metrics
  • extended parallelism, including parallel broadcasting to hierarchical data
  • composable time series classifiers, regressors, distances, time series aligners
  • reproducibility features such as blueprint and fitted estimator serialization
  • benchmarking frameworks for replicating studies such as M4/M5

Each feature vignette will come with links to further, extended tutorials where applicable.
sktime is developed by an open community, with aims of ecosystem integration in a neutral, charitable space. We welcome contributions and seek to provides opportunity for anyone worldwide.


Prior Knowledge Expected

No previous knowledge expected

three members of the community will co-present

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

I have completed my master's in Statistics from Indian Statistical Institute with Computational Statistics specialisation back in 2019. Currently I am pursuing my career as a Data Scientist at Publicis Sapient.

core developer and founder of sktime, a python open source library for ML with time series, and an openly governed community with charitable mission