Forecasting Financial Markets with Timestral

Timestral is Mistral’s new family of models for forecasting time series. Give it a sequence of numbers, such as daily sales or electricity demand, and it predicts what comes next. It also produces a range of possible values, so we can ask how uncertain the forecast is. The family runs from 5 million to 209 million parameters and supports multiple related series and extra inputs. We were interested in the 28-million-parameter version: small enough to run locally, and already trained across other time series before we gave it our market data. We tested Timestral on daily stock returns, trading volume and a measure of volatility, then fine-tuned the model on older market data. The whole experiment ran on a 16 GB M2 MacBook Air, without a GPU. Volume gave us the clearest result. In our 2026 research note, the original Timestral-28M reduced fifth-session stock log-volume error by 4.70% against a conditional regression. Fine-tuning moved that to 4.94%, but the direct comparison gave us…