Time Series Modeling of Market Price in Real-Time Bidding

Real-Time-Bidding (RTB) is one of the most popular online
advertisement selling mechanisms. Modeling the highly dynamic bidding
environment is crucial for making good bids. Market prices of auctions
fluctuate heavily within short time spans. State-of-the-art methods neglect
the temporal dependencies of bidders’ behaviors. In this paper, the bid
requests are aggregated by time and the mean market price per aggregated
segment is modeled as a time series. We show that the Long Short Term
Memory (LSTM) neural network outperforms the state-of-the-art univariate time series models by capturing the nonlinear temporal dependencies
in the market price. We further improve the predicting performance by
adding a summary of exogenous features from bid requests.