document: 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 .
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