time-series-models
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Timestral: Mistral release time-series foundation models
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7 min read
Mistral has released Timestral, a family of four time-series foundation models for probabilistic multivariate forecasting. The models range from 5 million to 209 million parameters, accept multivariate targets plus past-only and known-future covariates, and produce forecasts across 21 quantiles. Mistral says its 209M-parameter model ranks first among pretrained zero-shot models on the GIFT-Eval, TIME and fev-bench public benchmarks, using a single checkpoint without ensembling. Timestral is designed for uses such as sensor monitoring, capacity planning, energy, supply chains, finance and predictive maintenance. It can jointly model related signals, use categorical inputs such as machine states, and incorporate information known in advance, including production schedules, holidays or weather forecasts. The models have a 32,768-step context length and can run inference on CPUs: Mistral reports that forecasting one day from three weeks of 15-minute data takes 10 milliseconds for the 5M model and 140 milliseconds for the 209M model on an eight-thread server CPU. Training uses mostly synthetic data, followed by post-training on mixed real and synthetic data. Mistral offers zero-shot use, fine-tuning on proprietary data and deployment through Mistral Forge, including on-premises, VPC and air-gapped environments. A GitHub repository is available under a modified MIT license.