Abstract: Advertisement (“ad”) campaign forecasting may predict results for the campaign and the ads included in the campaign. The results may include a forecast for impressions, clicks, conversions, and/or interactions with the ads. The forecasting may be utilized for display advertising with non-guaranteed delivery (“NGD”) systems in which a bidding platform allows advertisers to bid for ad impressions, clicks, and/or conversions. Forecasting results may be used by advertisers to manage and optimize campaigns.

Abstract: Techniques are provided for advertiser bid forecasting in online advertising, including display advertising. Methods are provided in which key targeting-related user segments are determined from bidding statistics. A feature set is extracted from an impression opportunity, based at least in part on the bidding statistics. A gradient boosting descent tree technique is utilized in determining an initial bid forecasting result. A linear regression-based model is used in post-tuning to arrive at a post-tuned result. For short-term forecasting, this may be the final result. For long-term forecasting, a hybrid approach may be utilized with further processing including utilization of a publisher-specific model.