基于神经时间点过程的业务流程执行概率预测
Probabilistic Forecasting of Business Process Executions with Neural Temporal Point Processes
- Paris Dauphine University-PSL（巴黎多菲纳大学-PSL）
- University of Vienna（维也纳大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对业务流程执行预测，提出基于标记时间点过程的生成式模型，结合变换器与混合解码器处理并列时间戳，在保持点准确性的同时提升剩余时间分布校准性并降低推理成本。
AI中文摘要：
服务型系统的操作员基于对正在运行的执行将如何继续的预测采取行动，而此类预测只有在可靠性已知时才具有可操作性。针对此任务的主流深度学习模型是判别性和确定性的：它们输出单一的下一活动和单一的剩余时间估计，而没有可供推理的分布。我们转而将问题建模为带有标记时间点过程的生成式序列建模，该过程定义了下一标记及其事件间时间的联合密度，因此通过构造即可提供预测分布。真实事件日志违反了这些模型所依赖的简单点过程假设，因为连续事件经常携带相同的时间戳；我们显式处理此类并列事件，并将变换器编码器与事件间时间的混合解码器相结合，通过精确对数似然进行训练。在十个公开日志上，所得模型在点准确性上匹配判别性基线，在剩余时间分布的校准性和锐度上优于它们，并且在推理时成本最低，因为完整的预测分布可通过单次前向传播获得，无需采样。
英文摘要：
Operators of service-based systems act on forecasts of how a running execution will continue, and such a forecast is actionable only if its reliability is known. Mainstream deep-learning models for this task are discriminative and deterministic: they emit a single next activity and a single remaining-time estimate, without a distribution to reason over. We instead cast the problem as generative sequence modelling with marked temporal point processes, which define a joint density over the next mark and its inter-event time and therefore deliver predictive distributions by construction. Real event logs violate the simple-point-process assumption these models rest on, since consecutive events frequently carry identical timestamps; we handle such ties explicitly and combine a transformer encoder with a mixture decoder over inter-event times, trained by exact log-likelihood. On ten public logs, the resulting model matches discriminative baselines on point accuracy, dominates them on the calibration and sharpness of remaining-time distributions, and is the cheapest at inference, since a full predictive distribution is obtained in a single forward pass without sampling.
