Priors over Systems: Synthetic Pretraining for Spatio-Temporal Forecasting

Spatio-temporal foundation models aim to transfer across networks, but current pretraining relies on a limited collection of real systems. We ask whether a sufficiently expressive generative prior can replace real-system data collection with synthetic pretraining. MicroFlow generates synthetic systems in three separated stages: it draws a latent constitution that fixes how the system may behave, simulates its evolution from micro-level interactions, and then adds an observer that aggregates the granular trajectories into time series. We find that this separation of dynamics from observation makes the prior more expressive than naive functional series propagation. To demonstrate the usefulness of MicroFlow, we develop MicroFlow-ST, an architecture that models each modality (time series and graphs) with its own tower and couples them for spatio-temporal training. Without observing any real system during pretraining, our frozen checkpoints are competitive with similarly-sized state-of-the-art foundation models trained on both synthetic and real-world data, outperforming all of the ones we evaluate on zero-shot long-term spatio-temporal forecasting. Lightweight adaptation of MicroFlow-ST further lowers its MAE by 33% and 42% on average across short- and long-horizon tasks, respectively. We release the synthetic generator and all reported checkpoints.