Distribution Shift in Airline Customer Behavior during COVID-19

Autor:	Garg, Abhinav, Shukla, Naman, Marla, Lavanya, Somanchi, Sriram
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Computational Engineering Finance and Science Economics - Econometrics
Druh dokumentu:	Working Paper
Popis:	Traditional AI approaches in customized (personalized) contextual pricing applications assume that the data distribution at the time of online pricing is similar to that observed during training. However, this assumption may be violated in practice because of the dynamic nature of customer buying patterns, particularly due to unanticipated system shocks such as COVID-19. We study the changes in customer behavior for a major airline during the COVID-19 pandemic by framing it as a covariate shift and concept drift detection problem. We identify which customers changed their travel and purchase behavior and the attributes affecting that change using (i) Fast Generalized Subset Scanning and (ii) Causal Forests. In our experiments with simulated and real-world data, we present how these two techniques can be used through qualitative analysis. Comment: 6 pages, 5 figures, NeurIPS 2021 Workshop on Distribution Shifts: connecting methods and applications (DistShift)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2111.14938 Zobrazit plný text záznamu View this record from Arxiv