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6 May 2022 Analysis of air ticket characteristics based on random forest classification and autoregressive integrated moving average prediction
Weiyu Tang, Hui Ding, Hongliang Huang, Wei Lv
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Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 121762H (2022) https://doi.org/10.1117/12.2636583
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China
Abstract
With the rapid development of tourism, more and more people choose to travel by air, and the price fluctuation of air tickets is large and the law is not significant, which makes it necessary to use some big data technical means to predict the change law of prices. The research and analysis of the characteristics of air tickets are helpful to analyze the internal value of tourism. Based on the web crawler technology, this paper obtains a large number of flight information from Shanghai to Guangzhou. Through data exploration and preprocessing, Random Forest classification model is established to predict the purchase of air tickets. The prediction accuracy of whether to buy or not is 96.96%. Then, ARIMA model is established to predict the ticket price trend. The deviation rate of the model is less than 18%. The future ticket price can be effectively predicted through Time Series. The machine learning model established in this paper provides model support for the study of the characteristics of air tickets. The machine learning model established in this paper provides model support for the study of the characteristics of air tickets and technical support for the specified price strategy of the aviation industry, so as to promote the development of tourism.
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Weiyu Tang, Hui Ding, Hongliang Huang, and Wei Lv "Analysis of air ticket characteristics based on random forest classification and autoregressive integrated moving average prediction", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 121762H (6 May 2022); https://doi.org/10.1117/12.2636583
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KEYWORDS
Data modeling
Analytical research
Autoregressive models
Machine learning
Algorithm development
Data acquisition
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