Outliers detect methods for time series data

Liang, Tengfei; Cao, Chunxiang

doi:10.1080/09720529.2018.1479171

Cited by 4 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The choice of distribution is not arbitrary, and a bad choice of distribution will perform poorly. Liang and Cao (2018) choose to fit a Normal distribution to hotel booking data to detect anomalous observations.…”

Section: Univariate Approachesmentioning

confidence: 99%

Identifying and Responding to Outlier Demand in Revenue Management

Rennie,

Cleophas,

Sykulski

et al. 2019

Preprint

View full text Add to dashboard Cite

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. State-of-theart systems rely on analyst expertise to identify outlier demand both online (within the booking horizon) and offline (in hindsight). In light of the partial nature of revenue management data and censoring effects from inventory controls, so far, there exists little research on automating the detection of outlier demand. To remedy this, we propose a novel approach, which detects outliers using functional data analysis in combination with extrapolation via time-series forecasting. We evaluate the approach in a simulation framework, which generates outliers by manipulating the demand model. By evaluating the full feedback-driven system of forecast and optimisation, we generate insight on the asymmetric effects of positive and negative demand outliers in light of revenue management heuristics that do or do not account for customer choice. Furthermore, we quantify the value of both online and offline outlier detection. We show that identifying instances of outlier demand using our methodology, and adjusting the forecast in a timely fashion, substantially increases revenue compared to what is earned when ignoring outliers.

show abstract

Section: Univariate Approachesmentioning

confidence: 99%