document: To buy or not to buy : mining airfare data to minimize ticket purchase price As product prices become increasingly available on the World Wide Web , consumers attempt to understand how corporations vary these prices over time . However , corporations change prices based on proprietary algorithms and hidden variables ( e.g. , the number of unsold seats on a flight ) . Is it possible to develop data mining techniques that will enable consumers to predict price changes under these conditions ? This paper reports on a pilot study in the domain of airline ticket prices where we recorded over 12,000 price observations over a 41 day period . When trained on this data , Hamlet -- our multi-strategy data mining algorithm -- generated a predictive model that saved 341 simulated passengers $ 198,074 by advising them when to buy and when to postpone ticket purchases . Remarkably , a clairvoyant algorithm with complete knowledge of future prices could save at most $ 320,572 in our simulation , thus HAMLET 's savings were 61.8 % of optimal . The algorithm 's savings of $ 198,074 represents an average savings of 23.8 % for the 341 passengers for whom savings are possible . Overall , HAMLET saved 4.4 % of the ticket price averaged over the entire set of 4,488 simulated passengers . Our pilot study suggests that mining of price data available over the web has the potential to save consumers substantial sums of money per annum .
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