Saturn: A Time-Series Foundation Model for Classification and Event Forecasting

When monitoring a physical system the question is rarely what the next sensor value will be but whether an event is coming and when. Time-series foundation models are trained for the former, fitting the whole future window rather than the brief excursion that matters. We formulate event forecasting as horizon-conditioned binary classification and introduce Saturn, built on two principles. First, a self-supervised joint-embedding predictive objective pretrains the encoder under a dual mask: one contiguous block drawn from inside the window and one fixed at the window’s end, both taken from a single masking budget. A contrastive term over two augmented views replaces the prior or moving-average target that usually prevents collapse. One frozen encoder then serves both tasks, each through a light head. Second, the precursor is never assumed. We pretrain entirely on synthetic episodes in which every event is generated together with the precursor that announces it, beside a deliberately precursor-free stratum, so the precursor is known to be present and its contribution to a score can be isolated; and a real corpus enters evaluation only after the Precursor Check, a hypothesis test that it carries predictive signal before an event and that nothing outside the series supplies it. The corpora that pass form PrecursorBench, a benchmark for event forecasting on which every entry carries a precursor the check has validated. Saturn’s encoder is frozen after pretraining and each task reads it through a light probe. It is competitive on the UCR and UEA time-series classification archives and, on PrecursorBench, with foundation models an order of magnitude larger.