Forecasting with Sktime – Introduction and Advanced Features: Pipelines, Hierarchical and Probabilistic Forecasts, Deep Learning and Foundation Models


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 workshop gives a hands-on introduction to forecasting with sktime, including advanced features such as hierarchical and probabilistic forecasts , and an overview of different model categories, including classical statistical, ML, deep learning, and foundation models.

Session Outline:

The workshop will provide a hands-on introduction to basic forecasting with sktime and advanced topics:

1. introduction - common forecasting use cases; endogenous and exogenous data, horizons, basic vignette; simple statistical models, reduction to sklearn regressors; navigating the model marketplace

2. forecasting pipelines - feature extraction, transformations for endogenous and exogenous data

3. tuning, evaluation and automl - parameter fitters, seasonality/stationarity, backtesting based tuning, multiplexing and automl

4. advanced features: probabilistic and hierarchical forecasting; probabilistic metrics;

5. advanced model classes: global forecasters, deep learning based forecasters, foundation models

Background Knowledge:

At least beginner knowledge in python is expected. Notebooks can be run in the cloud, but ability to set up environments locally is recommended.


Franz Kiraly is the founder and a core developer of the open source framework sktime. Franz has worked for 10 years in statistics/AI faculty roles at University College London and ELTE, and has been a fellow of the Alan Turing Institute for five years, the UK's national institute for data science and AI. Research interests include software design for open source toolboxes, reproducibility, and quality assurance in the data and AI space.

Franz has held a number of technical principal roles across different sectors, with a focus on AI technology adoption, change management, and assurance of algorithms.

Franz has initiated and contributed to numerous open source software toolboxes in python, Julia, and R, most prominently the sktime toolbox, the most widely used framework toolbox for data science and AI with time series.

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