NDLI: A Data-Mining Approach to Travel Price Forecasting

Content Provider	IEEE Xplore Digital Library
Author	Wohlfarth, T. Clemencon, S. Roueff, F. Casellato, X.
Copyright Year	2011
Abstract	With the advent of yield management in the air travel industry, a large body of data-mining techniques have been developed over the last two decades for the purpose of increasing profitability of airline companies. The mathematical optimization strategies put in place resulted in price discrimination, similar seats in a same flight being often bought at different prices, depending on the time of the transaction, the provider, etc. It is the goal of this paper to consider the design of decision-making tools in the context of varying travel prices from the customer's perspective. Based on vast streams of heterogeneous historical data collected through the internet, we describe here two approaches to forecasting travel price changes at a given horizon, taking as input variables a list of descriptive characteristics of the flight, together with possible features of the past evolution of the related price series. Though heterogeneous in many respects ( e.g. sampling, scale), the collection of historical prices series is here represented in a unified manner, by marked point processes (MPP). State-of-the-art supervised learning algorithms, possibly combined with a preliminary clustering stage, grouping flights whose related price series exhibit similar behavior, can be next used in order to help the customer to decide when to purchase her/his ticket.
Starting Page	84
Ending Page	89
File Size	362642
Page Count	6
File Format	PDF
ISBN	9781457721342
DOI	10.1109/ICMLA.2011.11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Databases Computational modeling Time series analysis Companies Predictive models prediction Trajectory machine learning
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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