Predict the online auction's closing price using grey system theory

Lim, Deborah and Patricia Anthony and Ho, Chong Mun (2010) Predict the online auction's closing price using grey system theory. In: 2010 IEEE International Conference on Systems, Man and Cybernetics (SMC 2010), 10-13 October 2010, Istanbul, Turkey.

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The introduction of online auction has resulted in a rich collection of problems and issues especially in the bidding process such as the process of monitoring multiple auction houses, picking which auction to participate in, and making the right bid. If bidders are able to predict the closing price for each auction, they are able to make a better decision on the time, place and the amount they can bid for an item. However, predicting closing price for an auction is not easy since it is dependent on many factors such as the behaviour of each bidder, the number of the bidders participating in that auction as well as each bidder's reservation price. This paper reports on the development of a predictor agent that utilizes Grey System Theory GM (1, 1) to predict the online auction closing price in order to maximize the bidder's profit. The performance of this agent is compared with an Artificial Neural Network Predictor Agent (using Feedforward Backpropagation Prediction Model). The effectiveness of these two agents is evaluated using real eBay auction's data (Apple IPhone 8GB). ©2010 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Artificial neural network, Grey system theory, Online auction
Subjects: H Social Sciences > HF Commerce
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Divisions: SCHOOL > School of Engineering and Information Technology
SCHOOL > School of Science and Technology
Depositing User: ADMIN ADMIN
Date Deposited: 10 Mar 2011 16:26
Last Modified: 19 May 2015 11:04

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