Online Change-Point Detection in Sparse Time Series With Application to Online Advertising
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 advertis...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on systems, man, and cybernetics. Systems man, and cybernetics. Systems, 2019-06, Vol.49 (6), p.1141-1151 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1151 |
|---|---|
| container_issue | 6 |
| container_start_page | 1141 |
| container_title | IEEE transactions on systems, man, and cybernetics. Systems |
| container_volume | 49 |
| creator | Zhang, Jie Wei, Zhi Yan, Zhenyu Zhou, MengChu Pani, Abhishek |
| description | 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. |
| doi_str_mv | 10.1109/TSMC.2017.2738151 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_journals_2226185795</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8063392</ieee_id><sourcerecordid>2226185795</sourcerecordid><originalsourceid>FETCH-LOGICAL-c363t-1ddeaa5490c4b6a53c8613358071468fed3ef82387718d0d4e66b49a2bd70ca13</originalsourceid><addsrcrecordid>eNo9kFFLwzAUhYMoOOZ-gPgS8LkzN2nT9HFUncJkwiaCLyFrb7eMLa1JJvjv3dzY0z0P3zkXPkJugQ0BWPEwn72VQ84gH_JcKMjggvQ4SJVwLvjlOYO8JoMQ1owx4EoKJnvka-o21iEtV8YtMXlvrYv0ESNW0baOWkdnnfEB6dxukc7QWwz008YVHXXdxlbmH4stPe2M6h_00QbrljfkqjGbgIPT7ZOP56d5-ZJMpuPXcjRJKiFFTKCu0ZgsLViVLqTJRKUkCJEplkMqVYO1wEZxofIcVM3qFKVcpIXhizpnlQHRJ_fH3c633zsMUa_bnXf7l5pzLkFleZHtKThSlW9D8Njoztut8b8amD5Y1AeL-mBRnyzuO3fHjkXEM6-YFKLg4g804Wz9</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2226185795</pqid></control><display><type>article</type><title>Online Change-Point Detection in Sparse Time Series With Application to Online Advertising</title><source>IEEE Electronic Library (IEL)</source><creator>Zhang, Jie ; Wei, Zhi ; Yan, Zhenyu ; Zhou, MengChu ; Pani, Abhishek</creator><creatorcontrib>Zhang, Jie ; Wei, Zhi ; Yan, Zhenyu ; Zhou, MengChu ; Pani, Abhishek</creatorcontrib><description>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.</description><identifier>ISSN: 2168-2216</identifier><identifier>EISSN: 2168-2232</identifier><identifier>DOI: 10.1109/TSMC.2017.2738151</identifier><identifier>CODEN: ITSMFE</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Advertising ; Change detection ; Computer simulation ; Data models ; Mathematical models ; Media ; Model accuracy ; Noise measurement ; Online advertising ; online change-point detection ; Performance prediction ; Predictive models ; Return on investment ; sparse time series (TS) ; Time series ; Time series analysis</subject><ispartof>IEEE transactions on systems, man, and cybernetics. Systems, 2019-06, Vol.49 (6), p.1141-1151</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c363t-1ddeaa5490c4b6a53c8613358071468fed3ef82387718d0d4e66b49a2bd70ca13</citedby><cites>FETCH-LOGICAL-c363t-1ddeaa5490c4b6a53c8613358071468fed3ef82387718d0d4e66b49a2bd70ca13</cites><orcidid>0000-0001-6059-4267 ; 0000-0002-5408-8752 ; 0000-0003-0242-8812</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8063392$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8063392$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhang, Jie</creatorcontrib><creatorcontrib>Wei, Zhi</creatorcontrib><creatorcontrib>Yan, Zhenyu</creatorcontrib><creatorcontrib>Zhou, MengChu</creatorcontrib><creatorcontrib>Pani, Abhishek</creatorcontrib><title>Online Change-Point Detection in Sparse Time Series With Application to Online Advertising</title><title>IEEE transactions on systems, man, and cybernetics. Systems</title><addtitle>TSMC</addtitle><description>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.</description><subject>Advertising</subject><subject>Change detection</subject><subject>Computer simulation</subject><subject>Data models</subject><subject>Mathematical models</subject><subject>Media</subject><subject>Model accuracy</subject><subject>Noise measurement</subject><subject>Online advertising</subject><subject>online change-point detection</subject><subject>Performance prediction</subject><subject>Predictive models</subject><subject>Return on investment</subject><subject>sparse time series (TS)</subject><subject>Time series</subject><subject>Time series analysis</subject><issn>2168-2216</issn><issn>2168-2232</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kFFLwzAUhYMoOOZ-gPgS8LkzN2nT9HFUncJkwiaCLyFrb7eMLa1JJvjv3dzY0z0P3zkXPkJugQ0BWPEwn72VQ84gH_JcKMjggvQ4SJVwLvjlOYO8JoMQ1owx4EoKJnvka-o21iEtV8YtMXlvrYv0ESNW0baOWkdnnfEB6dxukc7QWwz008YVHXXdxlbmH4stPe2M6h_00QbrljfkqjGbgIPT7ZOP56d5-ZJMpuPXcjRJKiFFTKCu0ZgsLViVLqTJRKUkCJEplkMqVYO1wEZxofIcVM3qFKVcpIXhizpnlQHRJ_fH3c633zsMUa_bnXf7l5pzLkFleZHtKThSlW9D8Njoztut8b8amD5Y1AeL-mBRnyzuO3fHjkXEM6-YFKLg4g804Wz9</recordid><startdate>20190601</startdate><enddate>20190601</enddate><creator>Zhang, Jie</creator><creator>Wei, Zhi</creator><creator>Yan, Zhenyu</creator><creator>Zhou, MengChu</creator><creator>Pani, Abhishek</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-6059-4267</orcidid><orcidid>https://orcid.org/0000-0002-5408-8752</orcidid><orcidid>https://orcid.org/0000-0003-0242-8812</orcidid></search><sort><creationdate>20190601</creationdate><title>Online Change-Point Detection in Sparse Time Series With Application to Online Advertising</title><author>Zhang, Jie ; Wei, Zhi ; Yan, Zhenyu ; Zhou, MengChu ; Pani, Abhishek</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-1ddeaa5490c4b6a53c8613358071468fed3ef82387718d0d4e66b49a2bd70ca13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Advertising</topic><topic>Change detection</topic><topic>Computer simulation</topic><topic>Data models</topic><topic>Mathematical models</topic><topic>Media</topic><topic>Model accuracy</topic><topic>Noise measurement</topic><topic>Online advertising</topic><topic>online change-point detection</topic><topic>Performance prediction</topic><topic>Predictive models</topic><topic>Return on investment</topic><topic>sparse time series (TS)</topic><topic>Time series</topic><topic>Time series analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Jie</creatorcontrib><creatorcontrib>Wei, Zhi</creatorcontrib><creatorcontrib>Yan, Zhenyu</creatorcontrib><creatorcontrib>Zhou, MengChu</creatorcontrib><creatorcontrib>Pani, Abhishek</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on systems, man, and cybernetics. Systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhang, Jie</au><au>Wei, Zhi</au><au>Yan, Zhenyu</au><au>Zhou, MengChu</au><au>Pani, Abhishek</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Online Change-Point Detection in Sparse Time Series With Application to Online Advertising</atitle><jtitle>IEEE transactions on systems, man, and cybernetics. Systems</jtitle><stitle>TSMC</stitle><date>2019-06-01</date><risdate>2019</risdate><volume>49</volume><issue>6</issue><spage>1141</spage><epage>1151</epage><pages>1141-1151</pages><issn>2168-2216</issn><eissn>2168-2232</eissn><coden>ITSMFE</coden><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.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TSMC.2017.2738151</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-6059-4267</orcidid><orcidid>https://orcid.org/0000-0002-5408-8752</orcidid><orcidid>https://orcid.org/0000-0003-0242-8812</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 2168-2216 |
| ispartof | IEEE transactions on systems, man, and cybernetics. Systems, 2019-06, Vol.49 (6), p.1141-1151 |
| issn | 2168-2216 2168-2232 |
| language | eng |
| recordid | cdi_proquest_journals_2226185795 |
| source | IEEE Electronic Library (IEL) |
| subjects | Advertising Change detection Computer simulation Data models Mathematical models Media Model accuracy Noise measurement Online advertising online change-point detection Performance prediction Predictive models Return on investment sparse time series (TS) Time series Time series analysis |
| title | Online Change-Point Detection in Sparse Time Series With Application to Online Advertising |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-13T16%3A31%3A30IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Online%20Change-Point%20Detection%20in%20Sparse%20Time%20Series%20With%20Application%20to%20Online%20Advertising&rft.jtitle=IEEE%20transactions%20on%20systems,%20man,%20and%20cybernetics.%20Systems&rft.au=Zhang,%20Jie&rft.date=2019-06-01&rft.volume=49&rft.issue=6&rft.spage=1141&rft.epage=1151&rft.pages=1141-1151&rft.issn=2168-2216&rft.eissn=2168-2232&rft.coden=ITSMFE&rft_id=info:doi/10.1109/TSMC.2017.2738151&rft_dat=%3Cproquest_RIE%3E2226185795%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2226185795&rft_id=info:pmid/&rft_ieee_id=8063392&rfr_iscdi=true |