A Consumption-Generation Driven Deep Embedding Framework for Next Activity Prediction

Next activity prediction, a critical subtask of Predictive Process Monitoring (PPM), aims to forecast the upcoming activity of an ongoing process instance based on event logs generated by process-aware information systems, thereby enabling businesses to proactively mitigate potential risks. While recent deep sequence models have shown strong predictive capabilities, they often fail to explicitly capture the intrinsic dependencies that govern the conditions and impacts of event occurrences in business processes. To address this limitation, we propose a novel Place-Transition Embedding (PTE) framework that captures these dependencies through Petri-net–inspired token consumption and generation semantics. PTE employs a deep embedding network to simulate the evolution of markings, i.e., how each observed event drives token dynamics over places over time under our resource interpretation. In addition, we introduce a temporal scoring mechanism that ranks candidate activities by jointly considering resource feasibility and temporal urgency, improving both interpretability and predictive accuracy. Extensive experiments on six real-world datasets show that PTE achieves state-of-the-art performance for next-activity prediction. Moreover, PTE generalizes robustly under sparse training conditions, highlighting its practical applicability in real-world scenarios with limited business data.