Predicting Air Ticket Demand using Deep Neural Networks

Kodai Imanaka and Chiaki Sakama

Proceedings of IEEE International Conference on Big Data, pages 2901-2908, 2021.


Predicting air ticket demand is crucial for both airline companies and travel agencies, while the task is generally hard due to its dynamic nature and few attempts have been made to apply machine learning techniques for this purpose. This paper provides an empirical study for predicting airline tickets sales using deep neural networks. A new learning model is introduced by extending the Long Short-Term Memory (LSTM) for handling non-time series data as well as time series data. The proposed model is compared with the SARIMAX model that is used for forecasting time series data with seasonal patterns. We perform experiments using real data and show that the proposed model captures demand changes better than the SARIMAX. In particular, features related to the day of the week and different airlines are well predicted.

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