FENStatS News

ECAS-ENBIS Course "Adaptive Machine Learning for Time Series Forecasting" is a half-day course that arose as a joint initiative from ENBIS and ECAS that has organized courses since 1987 to achieve training in special areas of statistics for both researchers and teachers for universities and professionals in industry fields. The course is scheduled for Sunday, 6 Sept 2026, between 14:00 and 18:00.

Under the guidance of Yannig Goude (EDF, France), the course focuses on adaptive machine learning tools for time series forecasting. Drawing on research developed at EDF R&D, we will cover interpretable machine learning methods, such as Generalized Additive Models (GAMs) and Kalman-filtered GAMs, designed to adapt to non-stationary contexts (e.g., data drift, structural breaks). The second part of the course explores modern approaches, including foundation models for time series and tabular data, as well as online expert aggregation methods.

The methods exposited will be illustrated by using real-world datasets (electricity demand, renewable production, electricity prices). These datasets provide excellent examples of time-varying environments, reflecting longer-term changes in consumption habits and the increasing penetration of intermittent power generation.

This 4-hour applicative course will include about 1 hour of hands-on practice (notebook presentation).

More information is available here.