Predict the online auction's closing price using Grey System Theory

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).