Abstract
Online advertising delivers promotional marketing messages to consumers through online media. Advertisers often have the desire to optimize their advertising spending strategies in order to gain the highest return on investment and maximize their key performance indicator. To build accurate advertisement performance predictive models, it is crucial to detect the change-points in the historical data and apply appropriate strategies to address a data pattern shift problem. However, with sparse data, which is common in online advertising and some other applications, online change-point detection is very challenging. We present a novel collaborated online change-point detection method in this paper. Through efficiently leveraging and coordinating with auxiliary time series, we can quickly and accurately identify the change-points in sparse and noisy time series. Simulation studies as well as real data experiments have justified the proposed method's effectiveness in detecting change-points in sparse time series. Therefore, it can be used to improve the accuracy of predictive models.