Abstract
Auction markets provide centralized procedures for the exposure of purchase and sale orders to all market participants simultaneously. Online auctions have effectively created a large marketplace for participants to bid and sell products and services over the Internet. eBay pioneered the online auction in 1995. As the number of demand for online auction increases, the process of monitoring multiple auction houses, picking which auction to participate in, and making the right bid become a challenging task for the consumers. Hence, knowing the closing price of a given auction would be an advantage since this information will be useful and can be used to ensure a win in a given auction. However, predicting a closing price for an auction is not easy since it is dependent on many factors. This paper reports on a predictor agent that utilises the Grey System Theory to predict the closing price for a given auction. The performance of this predictor agent is compared with another well known technique which is the Artificial Neural Network. The effectiveness of these models is evaluated in a simulated auction environment.
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Lim, D., Anthony, P., Ho, C.M. (2008). Agent for Predicting Online Auction Closing Price in a Simulated Auction Environment. In: Ho, TB., Zhou, ZH. (eds) PRICAI 2008: Trends in Artificial Intelligence. PRICAI 2008. Lecture Notes in Computer Science(), vol 5351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89197-0_23
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DOI: https://doi.org/10.1007/978-3-540-89197-0_23
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