mistralai/Timestral-5M

Timestral-5M

Timestral is a family of time series foundation models for zero-shot forecasting, available in **5M**, **28M**, **123M**, and **209M** parameter versions. This repository contains the 5M checkpoint.

>[!Note] > For more information about our Timestral family, go to the main model page: Timestral-209M.

Model Details

• Size: 5M parameters.
• Architecture: patch-based transformer with time attention and variate attention, 8 layers, embedding dimension 192.
• Patching: 32 time steps per patch. Each patch is embedded from its values together with their missing-value indicators.
• Context length: up to 32,768 time steps (1,024 patches).
• Forecast horizon: Up to 1,024 time steps (32 patches) in a single forward pass. Longer horizons are forecast block by block, with each block's median forecast appended to the context.
• Outputs: 21 quantiles (0.01, 0.05, 0.1, 0.15, …, 0.85, 0.9, 0.95, 0.99).
• Inputs: univariate or multivariate target variates, plus past-only and known-future covariates, numeric or categorical (categorical covariates are target-encoded). Missing values are marked as NaN.
• Inference: PyTorch on CPU or GPU.

Benchmarks

Timestral-209M achieves state-of-the-art zero-shot accuracy across three major forecasting benchmarks: GIFT-Eval, TIME, and fev-bench.

Smaller Timestral models offer a trade-off between model size and forecasting performance. All Timestreal models outperform every other model at the respective model size. See the Pareto-front plot on the main model page.

Results across the Timestral family

| Benchmark | Metric | Timestral-5M | Timestral-28M | Timestral-123M | Timestral-209M | |---|---|---|---|---|---| | GIFT-Eval | MASE ↓ | 0.705 | 0.678 | 0.659 | 0.658 | | | CRPS ↓ | 0.479 | 0.461 | 0.448 | 0.446 | | TIME | MASE ↓ | 0.652 | 0.638 | 0.628 | 0.626 | | | CRPS ↓ | 0.545 | 0.534 | 0.525 | 0.522 | | fev-bench | SQL skill score ↑ | 46.19 | 48.42 | 50.18 | 50.39 | | | MASE skill score ↑ | 34.54 | 37.27 | 39.19 | 39.49 |

License

Models have been released under a Modified MIT license: See here.

*You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights*