{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/machine-learning-modeling-for-time-series","title":"Machine learning modeling for time series problem: Predicting flight ticket prices","arxiv_id":"1705.07205","date":"2017-05-19","proceeding":null,"authors":["Jun Lu"],"abstract":"Machine learning has been used in all kinds of fields. In this article, we\nintroduce how machine learning can be applied into time series problem.\nEspecially, we use the airline ticket prediction problem as our specific\nproblem. Airline companies use many different variables to determine the flight\nticket prices: indicator whether the travel is during the holidays, the number\nof free seats in the plane etc. Some of the variables are observed, but some of\nthem are hidden. Based on the data over a 103 day period, we trained our\nmodels, getting the best model - which is AdaBoost-Decision Tree\nClassification. This algorithm has best performance over the observed 8 routes\nwhich has 61.35$\\%$ better performance than the random purchase strategy, and\nrelatively small variance over these routes. And we also considered the\nsituation that we cannot get too much historical datas for some routes (for\nexample the route is new and does not have historical data) or we do not want\nto train historical data to predict to buy or wait quickly, in which problem,\nwe used HMM Sequence Classification based AdaBoost-Decision Tree Classification\nto perform our prediction on 12 new routes. Finally, we got 31.71$\\%$ better\nperformance than the random purchase strategy.","url_abs":"http://arxiv.org/abs/1705.07205v2","url_pdf":"http://arxiv.org/pdf/1705.07205v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"machine-learning-modeling-for-time-series","repo_url":"https://github.com/junlulocky/AirTicketPredicting","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}