Space Weather doesn’t just produce beautiful auroras: it can silently disrupt navigation systems, radio links, and satellite-based technologies we rely on every day.

Travelling Ionospheric Disturbances (TIDs) are wave-like structures in the ionosphere that affect GNSS accuracy and HF communications. From an ML perspective, forecasting TIDs is a challenging rare-event prediction problem involving imbalanced data and heterogeneous physical inputs.

In this talk, I will present an operational machine learning approach developed within the T-FORS project to forecast TID occurrence over Europe. The model is built using CatBoost and integrates data from space- and ground-based observations.

The talk focuses on model design and evaluation choices. In particular, I will show how SHAP can be used to debug model behaviour, validate feature relevance, and build trust in predictions in a high-risk operational context.

Along the way, I’ll share practical engineering lessons on:

• handling class imbalance,
• incorporating domain knowledge into ML pipelines,
• producing uncertainty-aware outputs via Conformal Prediction, and
• running interpretable models in real-time forecasting systems.

The talk is aimed at data scientists and ML practitioners interested in applied forecasting, interpretable models, uncertainty quantification and ML at the boundary between data and physics.