发表机构
University of Mannheim; SAP Signavio; Technical University of Clausthal（曼海姆大学; 思爱普 Signavio; 克劳斯塔尔工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究对比序列、表格基础模型与LLM在PPM任务的表现，发现序列模型下一个活动预测最优，表格模型在时间任务具竞争力，LLM性能滞后且成本更高。
AI 中文摘要
预测过程监控（PPM）利用事件日志预测运行中流程实例的未来，例如预测下一个活动、案例完成前的剩余时间或下一个事件的时间。近年来PPM研究主要由从头训练的深度序列模型主导，如长短期记忆（LSTM）模型，但基础模型方法，尤其是大语言模型（LLM），正越来越多地被用于PPM。与此同时，具备上下文学习能力的表格基础模型提供了一种有前景的替代方案，但尚未针对PPM进行系统基准测试。因此，目前仍不清楚经典的基于序列的模型在这一不断发展的领域是否仍具有竞争力。本文通过在多个数据集和预测任务上进行的受控基准测试，从概念和实证层面对这三种建模范式进行了比较。结果表明，序列模型在预测下一个活动时始终表现最佳，而表格基础模型在时间相关任务上具有竞争力，尽管成本更高，但LLM通常落后于前两者。
英文摘要
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.
CommentsAccepted at ECML PKDD 2026 Workshops
