On optimizing airline ticket purchase timing

Proper timing of the purchase of airline tickets is difficult even when historical ticket prices and some domain knowledge are available. To address this problem, we introduce an algorithm that optimizes purchase timing on behalf of customers and provides performance estimates of its computed action policy. Given a desired flight route and travel date, the algorithm uses machine-learning methods on recent ticket price quotes from many competing airlines to predict the future expected minimum price of all available flights. The main novelty of our algorithm lies in using a systematic feature-selection technique, which captures time dependencies in the data by using time-delayed features, and reduces the number of features by imposing a class hierarchy among the rawfeatures and pruning the features based on in-situ performance. Our algorithm achieves much closer to the optimal purchase policy than other existing decision theoretic approaches for this domain, and meets or exceeds the performance of existing feature-selection methods from the literature. Applications of our feature-selection process to other domains are also discussed.