This paper presents an automated approach for providing ranked lists of outliers in observed demand to support analysts in network revenue management. Such network revenue management, e.g. for railway itineraries, needs accurate demand forecasts. However, demand outliers across or in parts of a network complicate accurate demand forecasting, and the network structure makes such demand outliers hard to detect. We propose a two-step approach combining clustering with functional outlier detection to identify outlying demand from network bookings observed on the leg level. The first step clusters legs to appropriately partition and pool booking patterns. The second step identifies outliers within each cluster and uses a novel aggregation method across legs to create a ranked alert list of affected instances. Our method outperforms analyses that consider leg data without regard for network implications and offers a computationally efficient alternative to storing and analysing all data on the itinerary level, especially in highly-connected networks where most customers book multi-leg products. A simulation study demonstrates the robustness of the approach and quantifies the potential revenue benefits from adjusting demand forecasts for offer optimisation. Finally, we illustrate the applicability based on empirical data obtained from Deutsche Bahn.

翻译：本文提出了一种自动化方法，用于为观察到的需求提供异常值排序列表，以支持网络收益管理中的分析师。此类网络收益管理（例如铁路行程网络）需要准确的需求预测。然而，网络整体或其部分中的需求异常值会使准确需求预测变得复杂，且网络结构使得此类需求异常值难以检测。我们提出了一种结合聚类与函数型异常值检测的两步法，用于从腿级观察到的网络预订中识别异常需求。第一步对腿进行聚类，以适当划分和汇集预订模式。第二步在每个聚类内识别异常值，并采用一种新颖的跨腿聚合方法，生成受影响实例的排序预警列表。我们的方法优于不考虑网络影响的腿级数据分析，并为存储和分析行程级所有数据提供了一种计算高效的替代方案，尤其是在大多数客户预订多腿产品的高连通性网络中。仿真研究证明了该方法的稳健性，并量化了为优化产品供应而调整需求预测所带来的潜在收益。最后，我们基于从德国铁路公司获得的经验数据展示了该方法的适用性。