A final price prediction model for english auctions: A neuro-fuzzy approach

Chin Shien Lin, Shihyu Chou, Shih Min Weng, Yu Chen Hsieh

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Online auction has now been a popular mechanism in setting prices for internet users. However, auction price prediction, involving the modeling of uncertainty regarding the bidding process, is a challenging task primarily due to the variety of factors changing in auction settings. Even if all the factors were accounted for, there still exist uncertainties in human behavior when bidding in auctions. In this paper, three models, regression, neural networks and neuro-fuzzy, are constructed to predict the final prices of English auctions, using real-world online auction data collected from Yahoo-Kimo Auction. The empirical results show that the neuro fuzzy system can catch the complicated relationship among the variables accurately much better than the others, which is of great help for the buyers to avoid overpricing and for the sellers to facilitate the auction. Besides, the knowledge base obtained from neuro fuzzy provides the elaborative relationship among the variables, which can be further tested for theory building.

Original languageEnglish
Pages (from-to)599-613
Number of pages15
JournalQuality and Quantity
Volume47
Issue number2
DOIs
Publication statusPublished - 2013 Feb

Keywords

  • Neueo network
  • Neuro-fuzzy System
  • Online auction
  • Regression

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences(all)

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