{"id":"https://openalex.org/W3003250678","doi":"https://doi.org/10.1137/1.9781611976236.2","title":"Two-Sample Testing for Event Impacts in Time Series","display_name":"Two-Sample Testing for Event Impacts in Time Series","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3003250678","doi":"https://doi.org/10.1137/1.9781611976236.2","mag":"3003250678"},"language":"en","primary_location":{"id":"doi:10.1137/1.9781611976236.2","is_oa":true,"landing_page_url":"https://doi.org/10.1137/1.9781611976236.2","pdf_url":"https://epubs.siam.org/doi/pdf/10.1137/1.9781611976236.2","source":{"id":"https://openalex.org/S4306463922","display_name":"Society for Industrial and Applied Mathematics eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 SIAM International Conference on Data Mining","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://epubs.siam.org/doi/pdf/10.1137/1.9781611976236.2","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017984255","display_name":"Erik Scharw\u00e4chter","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164149","display_name":"Bonn Aachen International Center for Information Technology","ror":"https://ror.org/054zhq066","country_code":"DE","type":"facility","lineage":["https://openalex.org/I135140700","https://openalex.org/I155417937","https://openalex.org/I4210164149","https://openalex.org/I4923324"]},{"id":"https://openalex.org/I135140700","display_name":"University of Bonn","ror":"https://ror.org/041nas322","country_code":"DE","type":"education","lineage":["https://openalex.org/I135140700"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Erik Scharw\u00e4chter","raw_affiliation_strings":["Chair of Data Science and Data Engineering, Bonn-Aachen International Center for Information Technology, University of Bonn, Germany,"],"affiliations":[{"raw_affiliation_string":"Chair of Data Science and Data Engineering, Bonn-Aachen International Center for Information Technology, University of Bonn, Germany,","institution_ids":["https://openalex.org/I4210164149","https://openalex.org/I135140700"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053906729","display_name":"Emmanuel M\u00fcller","orcid":"https://orcid.org/0000-0002-5409-6875"},"institutions":[{"id":"https://openalex.org/I135140700","display_name":"University of Bonn","ror":"https://ror.org/041nas322","country_code":"DE","type":"education","lineage":["https://openalex.org/I135140700"]},{"id":"https://openalex.org/I4210164149","display_name":"Bonn Aachen International Center for Information Technology","ror":"https://ror.org/054zhq066","country_code":"DE","type":"facility","lineage":["https://openalex.org/I135140700","https://openalex.org/I155417937","https://openalex.org/I4210164149","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Emmanuel M\u00fcller","raw_affiliation_strings":["Chair of Data Science and Data Engineering, Bonn-Aachen International Center for Information Technology, University of Bonn, Germany,"],"affiliations":[{"raw_affiliation_string":"Chair of Data Science and Data Engineering, Bonn-Aachen International Center for Information Technology, University of Bonn, Germany,","institution_ids":["https://openalex.org/I4210164149","https://openalex.org/I135140700"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5017984255"],"corresponding_institution_ids":["https://openalex.org/I135140700","https://openalex.org/I4210164149"],"apc_list":null,"apc_paid":null,"fwci":1.0987,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.79057729,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"10","last_page":"18"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.719778299331665},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.7098926901817322},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6884773969650269},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.6653670072555542},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4942653179168701},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.44514909386634827},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.44258520007133484},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.44072335958480835},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4300433397293091},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35712119936943054},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3200404644012451},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.20457598567008972},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16679131984710693}],"concepts":[{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.719778299331665},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.7098926901817322},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6884773969650269},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.6653670072555542},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4942653179168701},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.44514909386634827},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.44258520007133484},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.44072335958480835},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4300433397293091},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35712119936943054},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3200404644012451},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.20457598567008972},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16679131984710693},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1137/1.9781611976236.2","is_oa":true,"landing_page_url":"https://doi.org/10.1137/1.9781611976236.2","pdf_url":"https://epubs.siam.org/doi/pdf/10.1137/1.9781611976236.2","source":{"id":"https://openalex.org/S4306463922","display_name":"Society for Industrial and Applied Mathematics eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 SIAM International Conference on Data Mining","raw_type":"book-chapter"},{"id":"pmh:oai:arXiv.org:2001.11930","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2001.11930","pdf_url":"https://arxiv.org/pdf/2001.11930","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1137/1.9781611976236.2","is_oa":true,"landing_page_url":"https://doi.org/10.1137/1.9781611976236.2","pdf_url":"https://epubs.siam.org/doi/pdf/10.1137/1.9781611976236.2","source":{"id":"https://openalex.org/S4306463922","display_name":"Society for Industrial and Applied Mathematics eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320508","host_organization_name":"Society for Industrial and Applied Mathematics","host_organization_lineage":["https://openalex.org/P4310320508"],"host_organization_lineage_names":["Society for Industrial and Applied Mathematics"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 SIAM International Conference on Data Mining","raw_type":"book-chapter"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.6000000238418579,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3003250678.pdf","grobid_xml":"https://content.openalex.org/works/W3003250678.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W165616942","https://openalex.org/W201361503","https://openalex.org/W1731771962","https://openalex.org/W2024105897","https://openalex.org/W2041782669","https://openalex.org/W2045526969","https://openalex.org/W2049239673","https://openalex.org/W2050523241","https://openalex.org/W2054779505","https://openalex.org/W2058148593","https://openalex.org/W2064499317","https://openalex.org/W2065919272","https://openalex.org/W2067377518","https://openalex.org/W2096023955","https://openalex.org/W2099111195","https://openalex.org/W2103448012","https://openalex.org/W2110097068","https://openalex.org/W2110176078","https://openalex.org/W2124499489","https://openalex.org/W2129159585","https://openalex.org/W2137630814","https://openalex.org/W2145339411","https://openalex.org/W2178225550","https://openalex.org/W2212660284","https://openalex.org/W2418133265","https://openalex.org/W2478708596","https://openalex.org/W2483100472","https://openalex.org/W2586262377","https://openalex.org/W2963153810","https://openalex.org/W3146166473","https://openalex.org/W3150584212","https://openalex.org/W3215037115","https://openalex.org/W4233460278","https://openalex.org/W4249684946"],"related_works":["https://openalex.org/W1828158523","https://openalex.org/W2049578243","https://openalex.org/W2000145235","https://openalex.org/W2122079181","https://openalex.org/W1985848810","https://openalex.org/W2889939530","https://openalex.org/W2399195672","https://openalex.org/W3121881699","https://openalex.org/W2748838164","https://openalex.org/W2066015000"],"abstract_inverted_index":{"In":[0,67,110,137],"many":[1],"application":[2],"domains,":[3,151],"time":[4,29,60,82,94,123,147,181],"series":[5,30,61,83,86,95,124,148],"are":[6],"monitored":[7],"to":[8,25,104,139,164],"detect":[9],"extreme":[10],"events":[11,35,64],"like":[12],"technical":[13],"faults,":[14],"natural":[15],"disasters,":[16],"or":[17,51,156,169],"disease":[18],"outbreaks.":[19],"Unfortunately,":[20],"it":[21,167],"is":[22,32,48,54,144,170],"often":[23],"non-trivial":[24],"select":[26],"both":[27],"a":[28,37,73,81,85,105,111,132,160],"that":[31,96,166],"informative":[33],"about":[34],"and":[36,62,84,197],"powerful":[38],"detection":[39,41,46,108],"algorithm:":[40],"may":[42],"fail":[43],"because":[44,52],"the":[45,59,63,118,122,186],"algorithm":[47],"not":[49],"suitable,":[50],"there":[53],"no":[55],"shared":[56,78],"information":[57,79,98],"between":[58,80],"of":[65,87,117,121,189],"interest.":[66],"this":[68],"work,":[69],"we":[70,113],"thus":[71],"propose":[72],"non-parametric":[74],"statistical":[75],"test":[76,91,114],"for":[77,115,146,179],"observed":[88],"events.":[89],"Our":[90],"allows":[92],"identifying":[93],"carry":[97],"on":[99,171,176,192],"event":[100,107,129],"occurrences":[101,130],"without":[102],"committing":[103],"specific":[106],"methodology.":[109],"nutshell,":[112],"divergences":[116],"value":[119],"distributions":[120],"at":[125],"increasing":[126],"lags":[127],"after":[128],"with":[131,173],"multiple":[133],"two-sample":[134],"testing":[135],"approach.":[136],"contrast":[138],"related":[140,174],"tests,":[141],"our":[142,177,190],"approach":[143,191],"applicable":[145],"over":[149],"arbitrary":[150],"including":[152],"multivariate":[153],"numeric,":[154],"strings":[155],"graphs.":[157],"We":[158,183],"perform":[159],"large-scale":[161],"simulation":[162],"study":[163],"show":[165],"outperforms":[168],"par":[172],"tests":[175],"task":[178],"univariate":[180],"series.":[182],"also":[184],"demonstrate":[185],"real-world":[187],"applicability":[188],"datasets":[193],"from":[194],"social":[195],"media":[196],"smart":[198],"home":[199],"environments.":[200]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}