Author

Morad Laglil

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Review Jul 2026

Foundation Models and Fine-Tuning: Toward a New Generation of Models for Time Series Forecasting

This work reviews the main architectures, pre-training strategies, and optimization methods underpinning foundation models for zero-shot time series forecasting, and investigates post-pre-training fine-tuning of selected foundation models to enhance their performance on specific datasets.

Morad Laglil, Bertrand Pracca, Emilie Devijver et al. · arXiv.org · 1 citation

#machine learning Preprint Aug 2026

Learning to Difference: Adaptive Reversible Differencing (AdaRDiff) for Time Series Forecasting

AdaRDiff is proposed, a generalized differencing approach that uses learnable weights to simplify the series through weighted differencing with previous time instants, and attains state-of-the-art forecast accuracy across eight benchmarks spanning electricity, weather, traffic, and energy, at negligible parameter cost.

Morad Laglil, Younes Hlal, Marouane El Hadari et al. · 0 citations