Online Change-Point Detection in Sparse Time Series With Application to Online Advertising
Online Change-Point Detection in Sparse Time Series With Application to Online Advertising
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DOI：
10.1109/tsmc.2017.2738151
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Jie Zhang;Zhi Wei;Zhenyu Yan;Mengchu Zhou;Abhishek Pani
中科院分区：
文献类型：
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作者：
Jie Zhang;Zhi Wei;Zhenyu Yan;Mengchu Zhou;Abhishek Pani
在线广告通过在线媒体向消费者传递促销营销信息。广告商通常希望优化其广告支出策略，以获得最高的投资回报并最大化其关键绩效指标。为了建立准确的广告效果预测模型，检测历史数据中的变化点并应用适当的策略来解决数据模式转变问题至关重要。然而，由于在线广告和其他一些应用中常见的稀疏数据，在线变化点检测非常具有挑战性。我们在本文中提出了一种新颖的协作在线变点检测方法。通过有效利用和配合辅助时间序列，我们可以快速准确地识别稀疏和噪声时间序列中的变化点。仿真研究和真实数据实验证明了所提出的方法在检测稀疏时间序列中的变化点方面的有效性。因此，它可以用来提高预测模型的准确性。
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.