Introducing Timestral
Mistral has released Timestral, a family of time‑series foundation models ranging from 5M to 209M parameters. The 209M model tops the GIFT‑Eval, TIME and fev‑bench leaderboards for zero‑shot forecasting, outperforming larger competitors with fewer parameters. Timestral delivers probabilistic, multivariate forecasts, supports categorical and known‑future covariates, and runs efficiently on CPU without GPU.
The Core Development
Today, we’re releasing Timestral, our first family of time-series foundation models. Our largest model, at just 209M parameters, ranks first on all three of the field's main public benchmarks, GIFT-Eval, TIME, and fev-bench, outperforming the previous state of the art with only two-thirds of the model parameters.
Timestral delivers probabilistic, multivariate forecasts and supports past-only and known-future covariates. Our family of models sits on the Pareto front of forecasting accuracy versus model size, delivering leading performance at each size. Results have been submitted to the GIFT-Eval and TIME leaderboards.
Time series is one of the most common data types in enterprise, from sensor telemetry to observability metrics. Forecasts built on it drive decisions in capacity planning, energy, supply chains, risk and maintenance.
How It Works & Technical Architecture
Timestral has been optimised as a family of four models: 5M, 28M, 123M and 209M parameters. The larger models improve forecast performance, while the smaller ones are faster and cheaper. All four share the same architecture and training recipe. Every size sits on the Pareto front of accuracy versus model size: for each model size, no public model forecasts better.
Score vs model size on GIFT-Eval, TIME and fev-bench (shown as 1 − skill score). Lower is better. Our 28M model beats Chronos-2, about four times larger, on GIFT-Eval and TIME. Our 209M model beats TimesFM-3 and Falcon-2.0 with fewer parameters.
Strategic Perspective & Ecosystem Impact
Industrial plants forecast process sensors against the production schedule, catch equipment drift before it fails, and plan capacity and spare parts across thousands of assets. Finance teams forecast flows, volumes, and volatility to inform positions, hedging, and treasury across thousands of accounts.
Classical models such as ARIMA and linear regression remain well-established tools for forecasting time series. These models are usually fitted to a single series and learn only from that series' history.
Deep learning models such as PatchTST add the capacity to learn nonlinear patterns across many related series, but they are still typically trained from scratch on each dataset, so what they learn doesn't carry over to new domains.
Why it matters
The release demonstrates that small, CPU‑friendly models can match or exceed larger, GPU‑heavy baselines, lowering barriers to deploying advanced forecasting in industry. It also shows the viability of pre‑trained, zero‑shot time‑series models that transfer across domains.