Mistral's 209M time-series model tops three public forecasting benchmarks, and it runs a one-day forecast on a CPU in 140 ms
Mistral released Timestral, its first family of time-series foundation models, whose largest version at 209M parameters ranks first on the public GIFT-Eval, TIME and fev-bench forecasting benchmarks while using two-thirds the parameters of the previous state of the art. It produces probabilistic multivariate forecasts with past-only and known-future covariates, spans four sizes from 5M to 209M parameters, and needs no GPU: on an 8-thread server CPU, a one-day-ahead forecast from three weeks of 15-minute history takes 10 ms at 5M and 140 ms at 209M.