To buy or not to buy: Mining airfare data to minimize ticket purchase price

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2003, Pages 119-128

  Etzioni, Oren ^a   Tuchinda, Rattapoom ^a   Knoblock, Craig A ^b   Yates, Alexander ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

^b UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

Airline price prediction; Internet; Price mining; Web mining

Indexed keywords

AIRLINE PRICE PREDICTION; AIRLINE TICKETS; DATA MINING ALGORITHM; DATA MINING TECHNIQUES; HIDDEN VARIABLE; PILOT STUDIES; PREDICTIVE MODELS; PRICE CHANGES; PRICE PREDICTION; PRODUCT PRICE; PURCHASE PRICE; TICKET PRICES; WEB MINING;

ALGORITHMS; COMPUTER SIMULATION; DATA MINING; FORECASTING; INTERNET; WORLD WIDE WEB;

COSTS;

EID: 77952334887     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/956750.956767     Document Type: Conference Paper

References (28)

1. R. Agrawal and R. Srikant. Mining sequential patterns. In P. S. Yu and A. S. P. Chen, editors, Eleventh International Conference on Data Engineering, pages 3-14, Taipei, Taiwan, 1995. IEEE Computer Society Press.
2. J. L. Ambite, G. Barish, C. A. Knoblock, M. Muslea, J. Oh, and S. Minton. Getting from here to there: Interactive planning and agent execution for optimizing travel. In Proceedings of the Fourteenth Conference on Innovative Applications of Artificial Intelligence (IAAI-2002), pages 862-869, AAAI Press, Menlo Park, CA, 2002.
3. G. Barish and C. A. Knoblock. An efficient and expressive language for information gathering on the web. In Proceedings of the AIPS-2002 Workshop on Is there life after operator sequencing? - Exploring real world planning, pages 5-12, Tolouse, France, 2002.
4. D. Berndt and J. Clifford. Finding patterns in time series: a dynamic programming approach. In U. Fayyad, G. Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining. AAAI Press, 1996.
5. L. Breiman. Bagging predictors. Machine Learning, 24:123-140, 1996.
6. H. Chalupsky, Y. Gil, C. A. Knoblock, K. Lerman, J. Oh, D. V. Pynadath, T. A. Russ, and M. Tambe. Electric elves: Applying agent technology to support human organizations. In Proceedings of the Conference on Innovative Applications of Artificial Intelligence, 2001.
7. C. Chatfield. The Analysis of Time Series: An Introduction. Chapman and Hall, London, UK, 1989.
8. W. W. Cohen. Fast effective rule induction. In A. Prieditis and S. Russell, editors, Proc. of the 12th International Conference on Machine Learning, pages 115-123, Tahoe City, CA, July 9-12, 1995. Morgan Kaufmann.
9. F. Diebold. Elements of Forecasting. South-Western College Publishing, 2nd edition, 2000.
10. P. Domingos. MetaCost: A general method for making classifiers cost-sensitive. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 155-164, San Diego, CA, 1999. ACM Press.
11. R. Doorenbos, O. Etzioni, and D. Weld. A scalable comparison-shopping agent for the World-Wide Web. In Proc. First Intl. Conf. Autonomous Agents, pages 39-48, 1997.
12. Y. Freund and R. E. Schapire. Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference on Machine Learning, pages 148-156, Bari, Italy, 1996. Morgan Kaufmann.
13. C. W. J. Granger. Forecasting in Business and Economics. Harcourt Brace, second edition, 1989.
14. J. C. Hull. Options, Futures, and Other Derivatives. Prentice Hall College Div, 5th edition, 2002.
15. C. A. Knoblock, K. Lerman, S. Minton, and I. Muslea. Accurately and reliably extracting data from the web: A machine learning approach. In P. S. Szczepaniak, J. Segovia, J. Kacprzyk, and L. A. Zadeh, editors, Intelligent Exploration of the Web, pages 275-287. Springer-Verlag, Berkeley, CA, 2003.
16. N. Littlestone and M. K. Warmuth. The weighted majority algorithm. Information and Computation, 108 (2):212-261, February 1994.
17. D. Lucking-Reiley, D. Bryan, N. Prasad, and D. Reeves. Pennies from ebay: The determinants of price in online auctions. Technical report, University of Arizona, 2000.
18. S. Mahadevan. Average reward reinforcement learning: Foundations, algorithms, and empirical results. Machine Learning, 22 (1-3): 159-195, 1996.
19. S. McCartney. Airlines Rely on Technology To Manipuate Fare Structure. Wall Street Journal, November 3 1997.
20. J. Moody and M. Saffell. Reinforcement learning for trading systems and portfolios. In KDD, pages 279-283, 1998.
21. J. Moody and M. Saffell. Minimizing downside risk via stochastic dynamic programming. In Y. S. Abu-Mostafa, B. LeBaron, A. W. Lo, and A. S. Weigend, editors, Computational Finance 1999, Cambridge, MA, 2000. MIT Press.
22. J. Moody and M. Saffell. Learning to trade via direct reinforcement. In IEEE Transactions on Neural Networks, Vol. 12, No. 4, 2001.
23. J. F. Roddick and M. Spiliopoulou. A bibliography of temporal, spatial and spatio-temporal data mining research. SIGKDD Explorations, 1 (1):34-38, 1999.
24. H. S. Shah, N. R. Joshi, A. Sureka, and P. R. Wurman. Mining for bidding strategies on ebay. In Lecture Notes in Artificial Intelligence. Springer-Verlag, 2003.
25. R. S. Sutton and A. Barto. Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA, 1998.
26. K. M. Ting and I. H. Witten. Issues in stacked generalization. Journal of Artificial Intelligence Research, 10:271-289, 1999.
27. M. P. Wellman, D. M. Reeves, K. M. Lochner, and Y. Vorobeychik. Price prediction in a trading agent competition. Technical report, University of Michigan, 2002.
28. D. Wolpert. Stacked generalization. Neural Networks, 5:241-259, 1992.