Real-Time Forecasting of Online Auctions via Functional K-Nearest Neighbors

Abstract

Forecasting the price in online auctions is important for buyers and sellers. With good forecasts, bidders can make informed bidding decisions and sellers can select the right time and place to list their products. While information from other auctions can help forecast an ongoing auction, it should be weighted by its relevance to the auction of interest. We propose a novel functional K-nearest neighbor (fKNN) forecaster for real-time forecasting of online auctions. The forecaster uses information from other auctions and weighs their contribution by their relevance in terms of auction, seller and product features, and by similarity of the price paths. We capture an auction's price path borrowing ideas from functional data analysis. We propose a novel Beta growth model, and then measure distances between two price paths via the Kullback-Leibler distance. Our resulting fKNN forecaster incorporates a mixture of functional and non-functional distances. We apply the forecaster to several large datasets of eBay auctions, showing improved predictive performance over several competing models. We also investigate performance across various levels of data heterogeneity, finding that fKNN is particularly effective for forecasting heterogeneous auction populations.