Predictor agent for online auction closing price
Online auction has given consumers a “virtual” flea market with all the new and used merchandises from around the world. Due to the increasing demand of online auction, consumers are faced with the problem of monitoring multiple auction houses, picking which auction to participate in, and making the...
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| Format: | Thesis |
| Language: | English English |
| Published: |
2009
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| Subjects: | |
| Online Access: | https://eprints.ums.edu.my/id/eprint/26956/1/Predictor%20agent%20for%20online%20auction%20closing%20price.pdf https://eprints.ums.edu.my/id/eprint/26956/3/Predictor%20agent%20for%20online%20auction%20closing%20price%2024.pdf |
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| Summary: | Online auction has given consumers a “virtual” flea market with all the new and used merchandises from around the world. Due to the increasing demand of online auction, consumers are faced with the problem 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, then they are able to make a better decision on the
time, place and the amount they can bid for an item. However, predict closing price for an auction is not easy since it is dependent on many factors such as the behaviour and the number of the bidders. This thesis investigates one of the methods used in predicting the closing price of an auction called the Grey System Theory. This method has been
known to accurately speculate values in areas where the information is insufficient. Three other predictor methods are compared with Grey System Theory which are Time Series, Artificial Neural Network and Simple Exponential Function. These four prediction methods are then applied into different agent. The Grey System Agent is compared with other
prediction agents namely the Time Series Agent, the Artificial Neural Network Agent and the Simple Exponential Function Agent. The effectiveness of these agents is evaluated using a simulated auction environment as well as real data obtained from eBay. In conclusion, Grey System Agent is able to predict well in simulated marketplace and eBay. Besides that, moving observation increased the performance of the prediction |
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