mistralai/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.

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