Forecasting eBay’s Online Auction Prices using Functional Data Analysis

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 pointof-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; 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 the price 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 towards the auction end, and we tie this finding together with existing auction theory.