Adaptive Seasonality Estimation for Campaign Optimization in Online Advertising

Feedback control methodologies have provided scalable solutions to many optimization problems encountered in online programmatic advertising systems. This paper is concerned with the identification of seasonality in Internet user traffic of an advertising campaign, critical for optimal budget delivery and performance management. The seasonality typically manifests itself as a time-of-day (TOD) periodic pattern, which in this paper is modeled by a truncated Fourier series. An adaptive estimation scheme is proposed for the identification of the parameters, running alongside a feedback controller for the advertising campaign. Effectiveness and robustness of the proposed scheme are demonstrated with both simulation and experiment results from real advertising campaigns with the Demand Side Platform (DSP) at Verizon Media.