Handling forecast errors while bidding for display advertising

Most of the online advertising today is sold via an auction, which requires the advertiser to respond with a valid bid within a fraction of a second. As such, most advertisers employ bidding agents to submit bids on their behalf. The architecture of such agents typically has (1) an offline optimization phase which incorporates the bidder's knowledge about the market and (2) an online bidding strategy which simply executes the offline strategy. The online strategy is typically highly dependent on both supply and expected price distributions, both of which are forecast using traditional machine learning methods. In this work we investigate the optimum strategy of the bidding agent when faced with incorrect forecasts. At a high level, the agent can invest resources in improving the forecasts, or can tighten the loop between successive offline optimization cycles in order to detect errors more quickly. We show analytically that the latter strategy, while simple, is extremely effective in dealing with forecast errors, and confirm this finding with experimental evaluations.