Timestral
Timestral is a family of four time series foundation models (5M, 28M, 123M, and 209M parameters) for probabilistic forecasting. The authors report state-of-the-art results on GIFT-Eval, TIME, and fev-bench, with Timestral-209M and Timestral-123M taking the top two places on every metric. The approach combines Decoupled Horizon Forecasting, native known-future covariate support within a causal architecture, and a two-stage pipeline pretraining on synthetic data before post-training on mixed real and synthetic data. Weights and inference code are said to be released.
Open original source huggingface.coOne primary source; other links are context, not independent corroboration.
- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
- Linked in sourceView arXiv page
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Timestral is a family of four time series foundation models (5M, 28M, 123M, and 209M parameters) for probabilistic forecasting. The authors report state-of-the-art results on GIFT-Eval, TIME, and fev-bench, with Timestral-209M and Timestral-123M taking the top two places on every metric. The approach combines Decoupled Horizon Forecasting, native known-future covariate support within a causal architecture, and a two-stage pipeline pretraining on synthetic data before post-training on mixed real and synthetic data. Weights and inference code are said to be released.
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Key findings from the stored brief
- Four models with 5M, 28M, 123M, and 209M parameters are presented for probabilistic forecasting.
- Timestral-209M and Timestral-123M reportedly take the top two places on every metric across the cited benchmarks.
- Decoupled Horizon Forecasting separates prediction tokens from causally encoded context so longer horizons train without masking context.
- Pretraining uses a large-scale synthetic corpus, followed by post-training on a mix of real and synthetic data.
Source claims not independently checked; benchmark results are self-reported by the authors.
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Why this may matter
If the reported benchmark gains hold, the work suggests synthetic pretraining and decoupled horizon training can yield strong forecasting foundation models, with open weights available for further use and evaluation.
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