  Revenue management strongly relies on accurate forecasts. Thus, when
extraordinary events cause outlier demand, revenue management systems need to
recognise this and adapt both forecast and controls. Many passenger transport
service providers, such as railways and airlines, control the sale of tickets
through revenue management. State-of-the-art systems in these industries rely
on analyst expertise to identify outlier demand both online (within the booking
horizon) and offline (in hindsight). So far, little research focuses on
automating and evaluating the detection of outlier demand in this context. To
remedy this, we propose a novel approach, which detects outliers using
functional data analysis in combination with time series extrapolation. We
evaluate the approach in a simulation framework, which generates outliers by
varying the demand model. The results show that functional outlier detection
yields better detection rates than alternative approaches for both online and
offline analyses. Depending on the category of outliers, extrapolation further
increases online detection performance. We also apply the procedure to a set of
empirical data to demonstrate its practical implications. By evaluating the
full feedback-driven system of forecast and optimisation, we generate insight
on the asymmetric effects of positive and negative demand outliers. We show
that identifying instances of outlier demand and adjusting the forecast in a
timely fashion substantially increases revenue compared to what is earned when
ignoring outliers.
