对比四种方法预测物流出库剩余时间，深度学习最准但浅层模型更省资源。
Remaining Time Prediction in Outbound Warehouse Processes: A Case Study (Short Paper)
- 用四种方法预测航空物流出库流程的剩余时间。
- 深度学习准确率最高，浅层模型在资源消耗上优势明显。
- 实证数据来自含16.9万条记录的真实事件日志，可公开共享。
预测性流程监控是流程挖掘的一个子领域，旨在预测正在进行的流程实例的未来状态。一个常见的预测目标是剩余时间，即流程完成前还将经过的时间。本文在一家航空业物流公司的实际出库流程中，对比了四种不同的剩余时间预测方法。该公司提供了包含169,523条流程轨迹的新型原始事件日志，我们可公开获取。结果显示，深度学习模型取得了最高的预测准确率，但传统提升类等浅层方法也表现出具有竞争力的准确性，且所需计算资源显著更少。
原文摘要 · Abstract (English)
Predictive process monitoring is a sub-domain of process mining which aims to forecast the future of ongoing process executions. One common prediction target is the remaining time, meaning the time that will elapse until a process execution is completed. In this paper, we compare four different remaining time prediction approaches in a real-life outbound warehouse process of a logistics company in the aviation business. For this process, the company provided us with a novel and original event log with 169,523 traces, which we can make publicly available. Unsurprisingly, we find that deep learning models achieve the highest accuracy, but shallow methods like conventional boosting techniques achieve competitive accuracy and require significantly fewer computational resources.
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