Collaborated online change-point detection in sparse time series for online advertising

Jie Zhang, Zhi Wei, Zhenyu Yan, Abhishek Pani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings - 15th IEEE International Conference on Data Mining, ICDM 2015
EditorsCharu Aggarwal, Zhi-Hua Zhou, Alexander Tuzhilin, Hui Xiong, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1099-1104
Number of pages6
ISBN (Electronic)9781467395038
DOIs
StatePublished - Jan 5 2016
Event15th IEEE International Conference on Data Mining, ICDM 2015 - Atlantic City, United States
Duration: Nov 14 2015Nov 17 2015

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2016-January
ISSN (Print)1550-4786

Other

Other15th IEEE International Conference on Data Mining, ICDM 2015
Country/TerritoryUnited States
CityAtlantic City
Period11/14/1511/17/15

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Keywords

  • Online advertising
  • Online change-point detection
  • Sparse time series

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