Author

Ambroise Odonnat

2 papers indexed here

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#artificial intelligence Jul 2026

Post-Training in Time Series Foundation Models: A Unifying Framework

Time series foundation models (TSFMs) have emerged as general-purpose models for time series analysis, but pretraining alone is often insufficient for reliable downstream deployment. Bridging this gap requires further intervention to handle domain shift, task heterogeneity, limited supervision, and computational constr...

Shi-Feng Xie, Ambroise Odonnat, Ze-Hao Xiao et al. · arXiv.org · 2 citations

#machine learning Preprint Sep 2026

Tabby: An Open Pretraining Recipe for Time Series Foundation Models

In this report, we release Tabby, a long context probabilistic time series foundation model, together with a complete and open recipe of how it was built. Tabby adopts an encoder-only patch Transformer architecture and concentrates the contributions on the data and the training procedure. The pretraining corpus combine...

Shi-Feng Xie, Bahaeddine Abdessalem, Ze-Hao Xiao et al. · 1 citation