Forecasting Online Auctions using Dynamic Models

Abstract

We propose a dynamic forecasting model for price in online auctions. One of the key features of our model is that it operates during the live-auction, generating real-time forecasts which makes it different from previous static models. Our model is also different with respect to how information about price is incorporated. While one part of the model is based on the more traditional notion of an auction's price-level, another part incorporates its dynamics in the form of price-velocity and -acceleration. In that sense, it incorporates key features of a dynamic environment such as an online auction. The use of novel functional data methodology allows us to measure, and subsequently include, dynamic price characteristics. We illustrate our model on a diverse set of eBay auctions across many different book categories. It achieves significantly higher prediction accuracy compared to standard approaches.