EventFM: Zero-Shot Forecasting of What Happens When

Foundation models promise train-once zero-shot forecasting, yet event forecasting remains far less developed than regular time-series forecasting. The obstacle is structural: event streams unfold as irregular occurrences in continuous time, lacking a shared grid, fixed output space, and large interoperable corpora that make regular time-series foundation models possible. Moreover, an event forecaster must predict both what happens next and when it happens by inferring sequence-local relations among event types and arrival times. These challenges motivate EventFM, a foundation model for sequence-level zero-shot forecasting of marked temporal point processes. EventFM's event-native architecture normalizes time within each history, constructs one candidate query for each observed mark, and jointly predicts the next mark and its conditional occurrence time. To resolve the pretraining problem, we introduce the Event Structural Simulator (EventSS), an on-the-fly synthetic prior over event relations. EventSS captures trigger-response relations, overlapping workflows, delayed and cancellable consequences, recurrence, and non-additive event interactions. We pretrain EventFM on an EventSS prior and evaluate it in a strict sequence-level zero-shot setting, where each prediction uses only the observed history of the target sequence. Across five real-world benchmarks and structured synthetic scenarios, a single frozen EventFM shows strong zero-shot transfer, while targeted ablations show that both the event-native architecture and EventSS prior are important for transfer.