mistralai/Timestral-209M

Timestral-209M

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 209M checkpoint.

Timestral supports univariate and multivariate forecasting, past-only and known-future covariates, and probabilistic predictions across 21 quantiles. It accepts up to 32,768 historical time steps and forecasts up to 1,024 steps in a single forward pass.

We’re releasing the source code in our GitHub repository to make it easy to run Timestral and reproduce our benchmark results.

Model Details

• Size: 209M parameters.
• Architecture: patch-based transformer with time attention and variate attention, 24 layers, embedding dimension 768.
• 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.

Timestral-209M compared with other models

| Benchmark | Metric | Timestral-209M | t0-gamma | TimesFM-3 | Toto-2.0-2.5B | Chronos-2 | |---|---|---|---|---|---|---| | GIFT-Eval | MASE ↓ | 0.658 | 0.661 | 0.667 | 0.696 | 0.698 | | | CRPS ↓ | 0.446 | 0.452 | 0.456 | 0.476 | 0.485 | | TIME | MASE ↓ | 0.626 | 0.637 | 0.640 | 0.642 | 0.662 | | | CRPS ↓ | 0.522 | 0.534 | 0.536 | 0.539 | 0.556 | | fev-bench | SQL skill score ↑ | 50.39 | 49.42 | 48.99 | 44.42 | 47.28 | | | MASE skill score ↑ | 39.49 | 37.99 | 37.82 | 32.54 | 35.50 |

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 |

The Pareto-front plot below illustrates the trade-off between model size and forecasting performance across the Timestral family.

!Pareto front of model size versus forecasting performance for the Timestral models

Usage

Install

Set up your environment:

pip install git+https://github.com/mistralai/timestral.git # Python >= 3.10, torch >= 2.4

Or from a clone with uv, using the versions pinned in uv.lock:

git clone https://github.com/mistralai/timestral.git && cd timestral
uv sync --frozen
source .venv/bin/activate

Run the model

Here is a simple example using synthetic data.

import numpy as np
import torch
from timestral import TimestralForecaster

model = TimestralForecaster.from_pretrained(
"mistralai/Timestral-209M",
device="cuda" if torch.cuda.is_available() else "cpu"
)

# Synthetic example: one series with 1,000 historical observations.
context = np.random.default_rng(42).standard_normal(1000).cumsum()
forecast = model.forecast(context, prediction_length=96)

median = forecast.median
lower, upper = forecast.quantile(0.1), forecast.quantile(0.9)

Check out our GitHub repository for more details!

Additional Resources

• Blog post
• GitHub repository
• Tech report: coming soon!

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*