Foundation Models for Temporal Systems: From Forecasting to World Modeling

We propose a one-day in-person workshop at NeurIPS 2026 on foundation models for temporal systems, organized around temporal world modeling: how time-series models should be trained, evaluated, and deployed when real-world temporal data is multimodal, asynchronous, event-driven, and multi-scale, and when models must reason beyond extrapolation under distribution shift. Progress on these settings is fragmented across the forecasting, foundation-model, multimodal, and generative-modeling communities; the workshop unifies them around four directions: (i) forecasting and simulation tasks, (ii) temporal data and environments, (iii) temporal models, and (iv) evaluation and reliability, with applications in climate, healthcare, and industrial forecasting. The program features nine confirmed invited speakers spanning academia and industry, contributed papers and posters managed through OpenReview, a moderated panel, and an open community discussion, with approximately 200-300 in-person attendees anticipated. Distinct from djacent forecasting and foundation-model workshops, it emphasizes generative simulation, counterfactual rollouts, action-conditioned prediction, and long-horizon trajectory consistency: what temporal world modeling adds beyond forecasting.