{"id":"https://openalex.org/W4320024284","doi":"https://doi.org/10.1109/bigdata55660.2022.10020675","title":"Verification of Sparsity in the Attention Mechanism of Transformer for Anomaly Detection in Multivariate Time Series","display_name":"Verification of Sparsity in the Attention Mechanism of Transformer for Anomaly Detection in Multivariate Time Series","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4320024284","doi":"https://doi.org/10.1109/bigdata55660.2022.10020675"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020675","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10020675","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082133880","display_name":"Chihiro Maru","orcid":null},"institutions":[{"id":"https://openalex.org/I26120043","display_name":"Ochanomizu University","ror":"https://ror.org/03599d813","country_code":"JP","type":"education","lineage":["https://openalex.org/I26120043"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Chihiro Maru","raw_affiliation_strings":["Ochanomizu University,Graduate School of Humanities and Sciences,Tokyo,Japan","Graduate School of Humanities and Sciences, Ochanomizu University, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ochanomizu University,Graduate School of Humanities and Sciences,Tokyo,Japan","institution_ids":["https://openalex.org/I26120043"]},{"raw_affiliation_string":"Graduate School of Humanities and Sciences, Ochanomizu University, Tokyo, Japan","institution_ids":["https://openalex.org/I26120043"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020710269","display_name":"Boris Brandherm","orcid":null},"institutions":[{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Boris Brandherm","raw_affiliation_strings":["German Research Center for Artificial Intelligence,Saarbr&#x00FC;cken,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"German Research Center for Artificial Intelligence,Saarbr&#x00FC;cken,Germany","institution_ids":["https://openalex.org/I33256026"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089123620","display_name":"Ichiro Kobayashi","orcid":"https://orcid.org/0000-0003-4956-4010"},"institutions":[{"id":"https://openalex.org/I26120043","display_name":"Ochanomizu University","ror":"https://ror.org/03599d813","country_code":"JP","type":"education","lineage":["https://openalex.org/I26120043"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ichiro Kobayashi","raw_affiliation_strings":["Ochanomizu University,Graduate School of Humanities and Sciences,Tokyo,Japan","Graduate School of Humanities and Sciences, Ochanomizu University, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ochanomizu University,Graduate School of Humanities and Sciences,Tokyo,Japan","institution_ids":["https://openalex.org/I26120043"]},{"raw_affiliation_string":"Graduate School of Humanities and Sciences, Ochanomizu University, Tokyo, Japan","institution_ids":["https://openalex.org/I26120043"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2076,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.46747946,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"408","last_page":"414"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9983000159263611,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9323999881744385,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.9138715267181396},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.78072190284729},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7711215019226074},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.7356451749801636},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.7162525057792664},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6253716945648193},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5648841261863708},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5602051615715027},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5267307758331299},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4614943265914917},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.44642919301986694},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4122297465801239},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39246076345443726},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09865215420722961}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.9138715267181396},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.78072190284729},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7711215019226074},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.7356451749801636},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.7162525057792664},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6253716945648193},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5648841261863708},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5602051615715027},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5267307758331299},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4614943265914917},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.44642919301986694},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4122297465801239},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39246076345443726},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09865215420722961},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020675","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bigdata55660.2022.10020675","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"2022 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6899999976158142,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1983874169","https://openalex.org/W2407991977","https://openalex.org/W2786827964","https://openalex.org/W2911200746","https://openalex.org/W2950361482","https://openalex.org/W2950858167","https://openalex.org/W3040266635","https://openalex.org/W3081497074","https://openalex.org/W3106543020","https://openalex.org/W3135550350","https://openalex.org/W4283318673","https://openalex.org/W4320013936","https://openalex.org/W4385245566","https://openalex.org/W6631190155","https://openalex.org/W6739901393","https://openalex.org/W6751674603","https://openalex.org/W6758488679"],"related_works":["https://openalex.org/W3128634608","https://openalex.org/W3083250332","https://openalex.org/W4308846859","https://openalex.org/W4313495868","https://openalex.org/W3192727092","https://openalex.org/W4320024284","https://openalex.org/W4213225422","https://openalex.org/W4225271014","https://openalex.org/W4304776568","https://openalex.org/W4283069166"],"abstract_inverted_index":{"Anomaly":[0,77],"detection":[1,64],"in":[2,10,37,57,79,164],"multivariate":[3,54,80],"time":[4,55,81,98,149,165],"series":[5,56,82,99,150,166],"has":[6,91],"been":[7],"attracting":[8],"attention":[9,89,137],"order":[11,58],"to":[12,46,59],"realize":[13],"continuous":[14],"stable":[15],"operation":[16],"of":[17,32,52,94,107,145,148,159],"systems.":[18],"As":[19],"systems":[20],"diversify":[21],"and":[22,30,88,97,120,151],"monitoring":[23],"targets":[24],"become":[25],"more":[26],"complex,":[27],"the":[28,38,105,143,146,157],"number":[29],"types":[31],"measurements":[33],"obtained":[34],"from":[35],"sensors":[36],"system":[39],"have":[40],"dramatically":[41],"increased.":[42],"It":[43],"is":[44],"necessary":[45],"instantly":[47],"process":[48],"a":[49,71,74,133],"large":[50],"amount":[51],"complex":[53],"determine":[60,125],"anomalies":[61,126],"with":[62,73,109,127,135,167],"high":[63,128],"accuracy.":[65,129],"In":[66],"this":[67],"paper,":[68],"we":[69,141],"proposed":[70],"Transformer":[72],"Discriminator":[75],"for":[76],"Detection":[78],"(TDAD).":[83],"Introducing":[84],"an":[85],"adversarial":[86],"training":[87],"mechanisms":[90],"improved":[92,142],"extractions":[93],"detailed":[95],"loss":[96],"features":[100],"during":[101],"model":[102],"training.We":[103],"compare":[104],"performance":[106],"TDAD":[108,134],"five":[110,116],"other":[111],"deep":[112],"learning":[113],"methods":[114],"on":[115],"publicly":[117],"available":[118],"datasets":[119],"demonstrate":[121],"that":[122],"it":[123],"can":[124],"Furthermore,":[130],"by":[131,155],"proposing":[132],"Sparse":[136],"mechanism":[138],"(called":[139],"STDAD),":[140],"interpretability":[144],"patterns":[147],"achieved":[152],"better":[153],"results":[154],"increasing":[156],"influence":[158],"strongly":[160],"relevant":[161],"data":[162],"points":[163],"long-term":[168],"dependencies.":[169]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
