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cs.LG2026
Learning to Difference: Adaptive Reversible Differencing (AdaRDiff) for Time Series Forecasting
Morad Laglil, Younes Hlal, Marouane El Hadari +2
Reliable long-horizon time series forecasting is an important yet difficult problem. Trends and seasonality introduce complex temporal structure that challenges learning-based fore…
cs.LG2026
Foundation Models and Fine-Tuning: Toward a New Generation of Models for Time Series Forecasting
Morad Laglil, Bertrand Pracca, Emilie Devijver +1
Inspired by recent breakthroughs in large language models for natural language processing, foundation models have emerged as a promising paradigm for zero-shot time series forecast…
cs.LG2025
Modèles de Fondation et Ajustement : Vers une Nouvelle Génération de Modèles pour la Prévision des Séries Temporelles
Morad Laglil, Emilie Devijver, Eric Gaussier +1
Inspired by recent advances in large language models, foundation models have been developed for zero-shot time series forecasting, enabling prediction on datasets unseen during pre…