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arXiv · 2511.22674

Mod\`eles de Fondation et Ajustement : Vers une Nouvelle G\'en\'eration de Mod\`eles pour la Pr\'evision des S\'eries Temporelles

Morad Laglil·Emilie Devijver·Eric Gaussier·Bertrand Pracca
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Abstract

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 pretraining. These large-scale models, trained on vast collections of time series, learn generalizable representations for both point and probabilistic forecasting, reducing the need for task-specific architectures and manual tuning. In this work, we review the main architectures, pretraining strategies, and optimization methods used in such models, and study the effect of fine-tuning after pretraining to enhance their performance on specific datasets. Our empirical results show that fine-tuning generally improves zero-shot forecasting capabilities, especially for long-term horizons.

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Morad Laglil, Emilie Devijver, Eric Gaussier, Bertrand Pracca. 2025-11-27. Mod\`eles de Fondation et Ajustement : Vers une Nouvelle G\'en\'eration de Mod\`eles pour la Pr\'evision des S\'eries Temporelles. https://arxiv.org/abs/2511.22674

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