Emmanuel Doumard

Quentin Besnard, Emmanuel Doumard, Nicolas Labroche et al.

Deep learning models have shown strong potential for time series forecasting, yet their deployment in real-world environmental monitoring remains challenging due to non-stationary dynamics and limited explainability. In this work, we investigate explainability as a central tool for understanding continual learning in adaptive time series forecasting, with Experience Replay strategies. We study neural forecasting architectures such as PatchMixer, PatchTST and DLinear, augmented with attention-based sampling mechanisms to support model adaptation over time. Explainability is leveraged through attention rollout and gradient-based attribution methods (Grad-CAM) to analyze both predictive behavior and sampling strategies within a continual learning framework. Experiments conducted on real-world piezometric time series exhibiting heterogeneous patterns and regime shifts show that analyzing model and sampling behaviors provides valuable insights into the dynamics of the continual learning framework. Beyond predictive performance, our results highlight the challenges and opportunities of using explainability to understand continual learning behaviors, revealing how attribution patterns evolve over time and how they can inform data selection and adaptation strategies in non-stationary forecasting scenarios.

Robin Cugny, Emmanuel Doumard, Elodie Escriva et al.

The use of machine learning has increased dramatically in the last decade. The lack of transparency is now a limiting factor, which the field of explainability wants to address. Furthermore, one of the challenges of data mining is to present the statistical relationships of a dataset when they can be highly non-linear. One of the strengths of supervised learning is its ability to find complex statistical relationships that explainability allows to represent in an intelligible way. This paper shows that explanations can be used to extract knowledge from data and shows how feature selection, data subgroup analysis and selection of highly informative instances benefit from explanations. We then present a complete data processing pipeline using these methods on medical data. -- -- L'utilisation de l'apprentissage automatique a connu un bond cette dernière décennie. Le manque de transparence est aujourd'hui un frein, que le domaine de l'explicabilité veut résoudre. Par ailleurs, un des défis de l'exploration de données est de présenter les relations statistiques d'un jeu de données alors que celles-ci peuvent être hautement non-linéaires. Une des forces de l'apprentissage supervisé est sa capacité à trouver des relations statistiques complexes que l'explicabilité permet de représenter de manière intelligible. Ce papier montre que les explications permettent de faire de l'extraction de connaissance sur des données et comment la sélection de variables, l'analyse de sous-groupes de données et la sélection d'instances avec un fort pouvoir informatif bénéficient des explications. Nous présentons alors un pipeline complet de traitement des données utilisant ces méthodes pour l'exploration de données médicales.