JSM 2005 - Toronto

Abstract #302913

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.



The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

Back to main JSM 2005 Program page
Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 517
Type: Contributed
Date/Time: Thursday, August 11, 2005 : 10:30 AM to 12:20 PM
Sponsor: Business and Economics Statistics Section
Abstract - #302913
Title: Forecasting eBay's Online Auction Prices Using Functional Data Analysis
Author(s): Shanshan Wang*+
Companies: University of Maryland
Address: 412 Ridge Road Apt9, Greenbelt, MD, 20770, United States
Keywords: functional data analysis ; smoothing ; online auctions ; price dynamics ; autoregressive models ; exponential smoothing
Abstract:

The goal of this work is to derive models for forecasting the final price of ongoing online auctions. This forecasting task is important not only to the participants of an auction who compete against each other for the lowest price, but also to designers of bidder-side agents. Forecasting prices in online auctions is challenging from a statistical point-of-view because traditional forecasting models do not apply. The reasons for this are three typical features of online auction data: a) unequally spaced bids; b) the limited time horizon of an auction; and c) the dynamics of bidding change drastically over time. We propose a dynamic forecasting model for the auction price that can overcome these challenges. We use modern functional data analysis methods that take into account the price velocity and acceleration as the basis for our forecasting model. We show that our model has high forecast accuracy and it outperforms traditional methods. Our results also allow for new statistical insight into auction forecasting. We find that the forecasting accuracy increases as we predict further into the future; that is, further toward the auction end.

  • The address information is for the authors that have a + after their name.
  • Authors who are presenting talks have a * after their name.

Back to the full JSM 2005 program

JSM 2005 For information, contact jsm@amstat.org or phone (888) 231-3473. If you have questions about the Continuing Education program, please contact the Education Department.
Revised March 2005