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.


Marc Rovira is a data scientist at Electrolux Group in Stockholm, with a strong focus on forecasting and time series analysis. He actively contributes to the sktime community as a council member and user representative. Prior to his industry experience, Marc completed a Ph.D. that explored the intersection of computational fluid mechanics, chemical engineering, and machine learning, with the aim of mitigating air pollution. His educational background also includes a master's degree in aerospace engineering.

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