Paper recorded by Signals 4 on 2026-10-27 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-27 on arXiv · recorded by Signals 4 on 2026-10-28
Category: cs.AI · 人工智能 · first seen 2026-10-28
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