Collaborated Online Change-Point Detection in Sparse Time Series for Online Advertising

Abstract

Online advertising delivers promotional marketing messages to consumers through online media. Advertisers often have the desire to optimize their advertising spending and strategies in order to maximize their KPI (Key performance indicator). To build accurate ad performance predictive models, it is crucial to detect the change-points in historical data and therefore apply appropriate strategies to address the data pattern shift. However, with sparse data, which is common in online advertising, online change-point detection often becomes challenging. We propose a novel collaborated online change-point detection method in this paper. Through efficiently leveraging and coordinating with auxiliary time series, it can quickly and accurately identify the change-points in sparse and noisy time series. Simulation studies as well as real data applications have demonstrated its effectiveness in detecting change-point in sparse time series and therefore improving the accuracy of predictive models.