Identification of Seasonality in Internet Traffic to Support Control of Online Advertising

January 1, 2019

Abstract

Feedback control is widely applied to the campaign management in online advertising. Learning the pattern of user traffic on Internet plays an important role in solving the control problem. In this paper, we focus on characterizingthe seasonality, e.g., time of day (TOD) pattern of Internet user traffic for individual ad campaign. We model the seasonality using a truncated Fourier series with a set of amplitude and phase parameters. These seasonality parameters are estimated in a Bayesian framework using a minimum mean square error (MMSE) estimator, with their prior distribution learnt from historical data of a large number of campaigns. The proposed Bayesian method is shown to be robust and renders sensible seasonality for campaigns of disparate noise levels.

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Publication Type

Paper

Conference / Journal Name

American Control Conference 2019

Authors

Hao He

Niklas Karlsson

BibTeX


@inproceedings{
    author = {},
    title = {‌Identification of Seasonality in Internet Traffic to Support Control of Online Advertising‌},
    booktitle = {Proceedings of American Control Conference 2019‌},
    year = {‌2019‌}
}