对比三种模型在流程预测中的表现，发现序列模型最擅长预测下一步操作。
Revisiting Predictive Process Monitoring in the Age of Foundation Models: A Comparative Study of Sequence, Tabular, and LLM Approaches
- 用统一基准测试三种模型：序列、表格型和大语言模型
- 序列模型在下一步活动预测上表现最佳，表格模型在时间类任务上更优
- 大语言模型虽强但代价高，实际应用中未必划算
预测性流程监控（PPM）利用事件日志预测正在进行的流程实例未来状态，例如预测下一步活动、案例完成剩余时间或下一次事件发生时间。近年来，深度序列模型（如LSTM）主导了该领域，但随着大语言模型（LLMs）和具备上下文学习能力的表格型基础模型的兴起，新的范式逐渐显现。然而，这些新型模型在PPM中的系统性评估仍缺乏。本文通过多数据集、多任务的受控基准，对序列模型、表格型基础模型和大语言模型进行了概念与实证比较。结果表明，序列模型在下一步活动预测任务中始终表现最优；表格型基础模型在时间相关任务上表现竞争力；而大语言模型虽然理论能力强，但性能普遍落后且成本更高。
原文摘要 · Abstract (English)
Predictive process monitoring (PPM) leverages event logs to forecast the future of running process instances, for instance, predicting the next activity, the remaining time until case completion, or the time to the next event. While PPM research in recent years has been dominated by deep sequence models trained from scratch, such as Long Short-Term Memory (LSTM) models, foundation-model approaches---particularly large language models (LLMs)---are increasingly explored for PPM. At the same time, tabular foundation models with in-context learning capabilities offer a promising alternative but have not yet been systematically benchmarked for PPM. Thus, it remains unclear whether classical sequence-based models remain competitive in this evolving landscape. This paper compares the three modeling paradigms both conceptually and empirically through a controlled benchmark across multiple datasets and prediction tasks. The results show that sequence models consistently perform best for next activity prediction, whereas tabular foundation models are competitive on temporal tasks, with LLMs usually lagging behind despite higher cost.
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