用于数据驱动过程模拟的统一粒子滤波长短期记忆网络（Unified PF-LSTM）

中文摘要 AI 辅助

数据驱动过程模拟旨在从历史事件日志中生成真实的案例轨迹，无需明确指定底层动态模型。深度序列模型可通过下一个活动概率和条件时间分布捕捉复杂的时间依赖关系。然而，事件日志仅提供底层过程状态的部分视图，通常仅记录活动完成情况，而无对应的服务开始时间。因此，相同的观测过程历史可能与多个合理的潜在过程条件一致，而标准循环模型将每个过程前缀压缩为单个确定性循环状态。我们提出统一粒子滤波长短期记忆网络（Unified PF-LSTM），该模型维护并顺序更新一组加权的循环状态假设。我们基于矩生成函数，使用其加权均值和学习特征来总结这种粒子信念。所得表示用于预测下一个活动的分类分布以及当前活动驻留时间的条件分位数。该框架从事件日志数据进行端到端训练，并在三个真实世界的急诊科数据集上进行评估。结果表明，所提出的框架在所有数据集上重现路由、持续时间和系统级行为方面始终优于所考虑的数据驱动基线，在复杂过程动态仅部分反映在可用事件日志中的设置中，尤其表现出显著优势。

英文摘要

Data-driven process simulation aims to generate realistic case trajectories from historical event logs without requiring an explicitly specified model of the underlying dynamics. Deep sequence models can capture complex temporal dependencies through next-activity probabilities and conditional time distributions. However, event logs provide only a partial view of the underlying process state, often recording activity completions without the corresponding service-start times. Consequently, the same observed process history may be consistent with multiple plausible latent process conditions, whereas standard recurrent models compress each process prefix into a single deterministic recurrent state. We propose a Unified Particle Filter LSTM (Unified PF-LSTM) that maintains and sequentially updates a weighted set of recurrent-state hypotheses. We summarize this particle belief using its weighted mean and learned features based on the moment-generating function. The resulting representation is used to predict a categorical distribution over the next activity and conditional quantiles of the current activity's sojourn time. The framework is trained end-to-end from event-log data and evaluated on three real-world emergency department datasets. The results show that the proposed framework consistently outperforms the considered data-driven baselines in reproducing routing, duration, and system-level behavior across all datasets, with particularly strong gains in settings where complex process dynamics are only partially reflected in the available event logs.

发表机构

• Rotman School of Management, University of Toronto（多伦多大学罗特曼管理学院）

机构由 AI 辅助整理，请以论文原文为准。