We introduce Timestral, a family of four time series foundation models for probabilistic forecasting, with 5M, 28M, 123M, and 209M parameters. Timestral sets a new state of the art on GIFT-Eval, TIME,
Timestral is a family of time series foundation models that achieves state-of-the-art probabilistic forecasting through decoupled horizon forecasting, native future covariate support, and synthetic pretraining followed by mixed post-training.
We introduce Timestral, a family of four time series foundation models for probabilistic forecasting, with 5M, 28M, 123M, and 209M parameters. Timestral sets a new state of the art on GIFT-Eval, TIME, and fev-bench, where Timestral-209M and Timestral-123M take the top two places on every metric. We achieve this with three ingredients: Decoupled Horizon Forecasting (DHF), which separates prediction tokens from the causally encoded context, so that longer horizons are trained without masking context and each observed value is both a forecast target and context for later forecasts; a covariate framework that natively supports known-future covariates within a causal architecture; and a…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Timestral was first seen on Hugging Face · Daily papers on Oct 29, 2026. Since then it has appeared in 9 checks on 2 different days. Its best position was #19 of 30, reached on Oct 30, 01:28 UTC. It dropped off the list after Oct 30, 01:28 UTC.
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Not at the moment. It was last on the list on Oct 30, 01:28 UTC, after 2 days of trending.
#19 on Hugging Face · Daily papers, on Oct 30, 01:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Oct 29, 2026 and has been on the list on 2 days (9 checks) since.