Decoupling prediction tokens from causal context and pretraining on synthetic data enables time series models to forecast longer horizons more accurately—a practical approach for building better forecasting systems.
Timestral is a family of time series foundation models (5M–209M parameters) that achieves state-of-the-art probabilistic forecasting by combining three key techniques: Decoupled Horizon Forecasting (which trains longer predictions without masking context), native support for known-future covariates, and a two-stage pretraining pipeline on synthetic then real data.