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arXiv Machine Learning By Parvin Malekzadeh, Opher Baron, Dmitry Krass

A Unified Particle Filter LSTM for Data-Driven Process Simulation

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The paper introduces a Unified Particle Filter LSTM (Unified PF‑LSTM) for data‑driven process simulation, which maintains a weighted set of recurrent‑state hypotheses to better capture latent process conditions from incomplete event logs. By summarizing this particle belief with a weighted mean and moment‑generating‑function features, the model predicts next‑activity probabilities and conditional sojourn‑time quantiles. Experiments on three real‑world emergency department datasets show that the framework consistently outperforms existing data‑driven baselines in reproducing routing, duration, and system‑level behavior, especially when process dynamics are only partially reflected in the logs.

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