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Ticket price forecasting based on machine learning

Abstract— With the development of the aviation industry and the improvement of people's living standard, more and more people choose the aircraft as their way to travel, but the airline adjusts the price according to the revenue management in real time. Due to the large fluctuations in ticket prices, the price forecast has practical application value. This paper proposes a combination algorithm, combining the time series algorithm and random forests algorithm to model the ticket price data, and realizes the accurate prediction of the ticket price. The experimental results show that the combination algorithm model is more reliable by comparing the forecasting results with the actual results of each price model. The model is helpful for passengers to buy tickets and to save money.

Index Terms— price forecasting, time series algorithm, random forests algorithm

Yuling Li
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, CHINA
Zhengmin L
National Computer Network Emergency Response Technical Team/Coordination Center of China, CHINA
Sujuan Qin
Institute of Information Engineering, Chinese Academy of Sciences, CHINA

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Cite: Yuling Li, Zhengmin Li, Sujuan Qin, "Ticket price forecasting based on machine learning," Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering, pp. 465-469, Beijing, 25-27 June, 2017.