Price forecasting using dynamic assessment of market conditions and agent's bidding behavior

Authors:
Preetinder Kaur;Madhu Goyal;Jie Lu
Affiliations:
DeSI lab, Centre for Quantum Computation and Intelligent Systems, School of Software, University of Technology, Australia;DeSI lab, Centre for Quantum Computation and Intelligent Systems, School of Software, University of Technology, Australia;DeSI lab, Centre for Quantum Computation and Intelligent Systems, School of Software, University of Technology, Australia
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Year:
2012

Citing 6
Cited 0

Autonomous Bidding Agents in the Trading Agent Competition

IEEE Internet Computing
Efficient E-Commerce Agent Design Based on Clustering eBay Data

WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
An Empirical Analysis of Bidding Behavior in Simultaneous Ascending-Bid Auctions

ICEE '10 Proceedings of the 2010 International Conference on E-Business and E-Government
An Automated and Data-Driven Bidding Strategy for Online Auctions

INFORMS Journal on Computing
User heterogeneity and its impact on electronic auction market design: an empirical exploration

MIS Quarterly
Pricing analysis in online auctions using clustering and regression tree approach

ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction

Quantified Score

Hi-index	0.00

Visualization

Abstract

Multiple online auctions need complex bidding decisions for selecting which auction to participate in, whether to place single or multiple bids, do early or late bidding and how much to bid. This paper designs a novel fuzzy dynamic bidding agent (FDBA) which uses a comprehensive method for initial price estimation and price forecasting. First, FDBA selects an auction to participate in and calculates its initial price based on clustering and bid selection approach. Then the price of the auction is forecasted based on the estimated initial price, attitude of the bidders to win the auction and the competition assessment for the late bidders using fuzzy reasoning technique. The experiments demonstrated improved price forecasting outcomes using the proposed approach.