Timestral：四个参数规格的时间序列基础模型开源
Timestral
一个时间序列预测的开源基础模型系列，209M 和 123M 在 GIFT-Eval 等三个基准上拿下前两名，权重直接能下。
Timestral 是一组面向概率预测的时间序列基础模型，提供 5M、28M、123M、209M 四种参数规格。其技术核心包括三项：Decoupled Horizon Forecasting（DHF）将预测 token 与因果编码的上下文分离，使模型能在不遮蔽上下文的情况下训练更长预测区间；一个在因果架构内原生支持已知未来协变量的框架；以及先在合成语料预训练、再用真实与合成混合数据后训练的两阶段流程。在 GIFT-Eval、TIME 和 fev-bench 三个基准上，Timestral-209M 和 Timestral-123M 在所有指标上包揽前两名。四个模型的权重已发布在 Hugging Face，推理代码托管在 GitHub。
Timestral
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.
