arXiv:2610.37585v1 Announce Type: new
Abstract: 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 two-stage pipeline that pretrains exclusively on a diverse, large-scale synthetic corpus before post-training on a mix of real and synthetic data with extended context. This report describes the model design and its benchmark results. Weights for all four models are available on Hugging Face and inference code on GitHub.
arxiv
Timestral open time series models claim top spots on forecasting benchmarks
Timestral is a family of four probabilistic time series foundation models with 5M, 28M, 123M, and 209M parameters. The design uses Decoupled Horizon Forecasting to separate prediction tokens from causally encoded context, a covariate framework for known-future covariates, and a two-stage pipeline that pretrains only on large-scale synthetic data before post-training on mixed real and synthetic data with longer context. The report says Timestral-209M and Timestral-123M rank first and second on every metric of GIFT-Eval, TIME, and fev-bench. Weights for all four models are on Hugging Face, with inference code on GitHub.
Open model weights plus a claimed new state of the art on major time series benchmarks give researchers and practitioners a concrete forecasting advance to evaluate.
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