  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.
