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A Unified Particle Filter LSTM for Data-Driven Process Simulation

arXiv · AI, language, vision and robotics · article · Sep 2, 2026 · UTC

Data-driven process simulation aims to generate realistic case trajectories from historical event logs without requiring an explicitly specified model of the underlying dynamics. Deep sequence models can capture complex temporal dependencies through next-activity probabilities and conditional time distributions. However, event logs provide only a partial view of the underlying process state, often recording activity completions without the corresponding service-start times. Consequently, the same observed process history may be consistent with multiple plausible latent process conditions, wher

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