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.

Time series foundation models bring the pre-training approach to time series. Trained on a large and diverse corpus, our model learns patterns that transfer across series, forecasting zero-shot on data it has never seen, even in domains where data is scarce.

Benchmark Results

We evaluated Timestral on the three main public forecasting benchmarks: GIFT-Eval, TIME and fev-bench. Among pre-trained, zero-shot models, it ranks first across all five metrics. These results come from a single checkpoint, without ensembling.

GIFT-Eval, top ten of 83 comparable pretrained and zero-shot entries. Lower is better.

TIME, top ten of the 35 leaderboard entries. Lower is better on both metrics.

fev-bench, top ten of the 37 leaderboard entries, by SQL skill score. Higher is better.

Family of models optimised for every specification

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.

Timestral does not need a GPU at inference. On a standard server CPU with 8 threads, forecasting one day ahead from three weeks of 15-minute history takes 10 ms with the 5M model and 140 ms with the 209M model. Smaller models trade some accuracy for speed: the 28M model is five times faster than the 209M model, and the 123M model comes within 0.2% of the 209M score at three quarters of its latency.

An industrial example

Consider an industrial plant with thousands of sensors across its equipment. Timestral can improve the forecasts on that data in several ways.

Multivariate forecasting. A pump's vibration, temperature and pressure don't move independently. A rise in one often explains or causes a change in another. Timestral models related series jointly, so it can use those dependencies instead of forecasting each sensor in isolation.

Probabilistic forecasting. A single predicted value may not show the full picture. Timestral predicts a full set of quantiles, so teams can see how wide the uncertainty is, set alert thresholds that reflect it, and plan maintenance around risk rather than a single number.

Categorical covariates. Some covariates take values from a set of categories rather than continuous numbers, such as the day of the week, or a machine's state (running, idle, under maintenance). Encoding these as numbers would impose an order or scale they don't have. Timestral takes them as categorical inputs directly to improve forecasts.

Known-future covariates. Some drivers of sensor behaviour are known in advance, such as production schedules, planned shutdowns, weather forecasts. Timestral can condition on them to improve forecast quality, so a scheduled load increase is expected rather than flagged as a surprise.

From Model to System

Timestral is a numerical model that can run inside existing monitoring pipelines, or be paired with our language models in multi-modal agentic workflows.

In the plant, that could look like this:

1. Timestral running continuously on the plant's sensor streams, detecting anomalies as they occur.
2. When it flags an anomaly on a pump, an agent embeds the signals and compares against embeddings of past sensor data to find similar historical failures.
3. The agent then starts sub-agents to run a root cause analysis, drawing on context from that plant, such as maintenance records, shift logs and operator notes, equipment manuals, past work orders, and readings from related equipment.
4. The agent drafts a suggested diagnosis based on the findings, with supporting evidence and possible next steps.

The plant manager gets a diagnosis and a plan backed by the sensor data, rather than just an alert.

Key technical contributions

Several ideas have been combined to produce these results. Each will be described in detail in the technical report.

1. Decoupled Horizon Forecasting. Causal decoders trained with contiguous patch masking, as in Toto 2.0 and TimesFM-3, mask out the values to be forecast, so they look like missing values and are also lost as context for later forecasts. Our approach, DHF, separates the two: the full history stays in the sequence, and learned forecast tokens read the history before them. Every observed value thus serves as both target and context, true gaps stay flagged as missing, and training horizon lengths and missing-data augmentation can be tuned independently of each other.
2. Covariate-aware attention. Each input series is assigned a role: target variate, past-only covariate or known-future covariate. Known-future covariates are visible across the whole window while targets stay causal, and information flows only from covariates to targets. Timestral can therefore use a holiday calendar or a weather forecast without leaking a target's own future into its forecast.
3. Synthetic-first data. The majority of the training data was synthetic. We first generated univariate processes with coherent temporal dynamics, including trends, seasonality and diverse noise patterns. We then used a multivariate synthesizer to construct multivariate series by introducing nonlinear dependencies between these processes.
4. Two-stage training. Timestral is first pre-trained at scale on purely synthetic data with context size 8192, then post-trained on a mix of real and synthetic data with a 4x longer context. The objective is a pinball loss over 21 quantiles, a discrete approximation of CRPS.

A model that can be adapted

Public benchmarks measure general performance, but production data can be significantly different in behavior and structure. It may also shift over time as markets change regime, equipment degrades and demand patterns move. The lasting value lies in being able to adapt a model to in-domain data and keep retraining it as the distribution moves.

For organisations that want to go further, Timestral is available through Mistral Forge, the same training pipeline we use for our language models. It supports:

• Zero-shot inference out of the box.
• Architectural extensions, such as domain-specific covariates.
• Fine-tuning on proprietary data, specialising to a domain while preserving general performance.
• Control over the training data, including adjusting the training mix, replaying pre-training data, and generating synthetic data samples to reflect the target domain. Full control over the data ensures that nothing we backtest was seen during pre-training.

The full pipeline can be deployed on our clients’ hardware, including air-gapped environments.

What’s next

We have published a GitHub repository so you can run Timestral on your own data. A technical report with the full results will follow. For more specialised forecasting problems, or to collaborate on research, please reach out to us.