Predicting the final prices of online auction items

With the prevalence of the Internet and e-commerce, the online exchange market, especially the online auction market develops very fast. The activities of online auction produce a large number of transaction data. If utilized properly, these data can be of great benefit to sellers, buyers and website administrator. Typically, the final price prediction results may help sellers optimize the selling price of their items and auction attributes. At the same time, part of the information asymmetry problems may be solved for buyers. Thus, transaction time can be shortened and cost can be saved. In this paper, we collect large amounts of historical exchange data from Eachnet, an online auction website most famous in China and use machine learning algorithms and traditional statistical methods to forecast the final prices of auction items. We propose an attribute construction method to overcome the problem that auction bid list changes dynamically. Some experiments are performed and the prediction results are discussed to verify the proposed solution.