{"id":"https://openalex.org/W4320024086","doi":"https://doi.org/10.1109/bigdata55660.2022.10021063","title":"Decomposed Transformer with Frequency Attention for Multivariate Time Series Anomaly Detection","display_name":"Decomposed Transformer with Frequency Attention for Multivariate Time Series Anomaly Detection","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4320024086","doi":"https://doi.org/10.1109/bigdata55660.2022.10021063"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10021063","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10021063","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":"conference-paper","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/A5082511131","display_name":"Shuxin Qin","orcid":"https://orcid.org/0000-0003-4123-1991"},"institutions":[{"id":"https://openalex.org/I4210155350","display_name":"Purple Mountain Laboratories","ror":"https://ror.org/04zcbk583","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210155350"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuxin Qin","raw_affiliation_strings":["Purple Mountain Laboratories,Nanjing,China","Purple Mountain Laboratories, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purple Mountain Laboratories,Nanjing,China","institution_ids":["https://openalex.org/I4210155350"]},{"raw_affiliation_string":"Purple Mountain Laboratories, Nanjing, China","institution_ids":["https://openalex.org/I4210155350"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075760908","display_name":"Jing Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210155350","display_name":"Purple Mountain Laboratories","ror":"https://ror.org/04zcbk583","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210155350"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Zhu","raw_affiliation_strings":["Purple Mountain Laboratories,Nanjing,China","Purple Mountain Laboratories, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purple Mountain Laboratories,Nanjing,China","institution_ids":["https://openalex.org/I4210155350"]},{"raw_affiliation_string":"Purple Mountain Laboratories, Nanjing, China","institution_ids":["https://openalex.org/I4210155350"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100411794","display_name":"Dan Wang","orcid":"https://orcid.org/0000-0002-0921-2726"},"institutions":[{"id":"https://openalex.org/I4210155350","display_name":"Purple Mountain Laboratories","ror":"https://ror.org/04zcbk583","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210155350"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dan Wang","raw_affiliation_strings":["Purple Mountain Laboratories,Nanjing,China","Purple Mountain Laboratories, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purple Mountain Laboratories,Nanjing,China","institution_ids":["https://openalex.org/I4210155350"]},{"raw_affiliation_string":"Purple Mountain Laboratories, Nanjing, China","institution_ids":["https://openalex.org/I4210155350"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004963688","display_name":"Liang Ou","orcid":"https://orcid.org/0000-0003-2419-5634"},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]},{"id":"https://openalex.org/I4387153335","display_name":"China Telecom","ror":"https://ror.org/05p67dv18","country_code":null,"type":"company","lineage":["https://openalex.org/I4387153335"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Ou","raw_affiliation_strings":["China Telecom Research Institute,Guangzhou,China","China Telecom Research Institute, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Telecom Research Institute,Guangzhou,China","institution_ids":["https://openalex.org/I4210136246","https://openalex.org/I4387153335"]},{"raw_affiliation_string":"China Telecom Research Institute, Guangzhou, China","institution_ids":["https://openalex.org/I4210136246","https://openalex.org/I4387153335"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073718037","display_name":"Hongxin Gui","orcid":null},"institutions":[{"id":"https://openalex.org/I4210136895","display_name":"Institute for the Future","ror":"https://ror.org/049tcsg76","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I4210136895"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongxin Gui","raw_affiliation_strings":["Shandong Future Network Research Institute,Jinan,China","Shandong Future Network Research Institute, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Future Network Research Institute,Jinan,China","institution_ids":["https://openalex.org/I4210136895"]},{"raw_affiliation_string":"Shandong Future Network Research Institute, Jinan, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019328174","display_name":"Gaofeng Tao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210155350","display_name":"Purple Mountain Laboratories","ror":"https://ror.org/04zcbk583","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210155350"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gaofeng Tao","raw_affiliation_strings":["Purple Mountain Laboratories,Nanjing,China","Purple Mountain Laboratories, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purple Mountain Laboratories,Nanjing,China","institution_ids":["https://openalex.org/I4210155350"]},{"raw_affiliation_string":"Purple Mountain Laboratories, Nanjing, China","institution_ids":["https://openalex.org/I4210155350"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.0243,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.89233058,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1090","last_page":"1098"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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":1.0,"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":0.9995999932289124,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9866999983787537,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7282899618148804},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6990403532981873},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5599389672279358},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5559784173965454},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5452979803085327},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5451028943061829},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5374940037727356},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.533930778503418},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5260108709335327},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5243989825248718},{"id":"https://openalex.org/keywords/time\u2013frequency-analysis","display_name":"Time\u2013frequency analysis","score":0.4233437180519104},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3727593719959259},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3418543338775635},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11373117566108704},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.10076585412025452},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.09720861911773682}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7282899618148804},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6990403532981873},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5599389672279358},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5559784173965454},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5452979803085327},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5451028943061829},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5374940037727356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.533930778503418},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5260108709335327},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5243989825248718},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.4233437180519104},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3727593719959259},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3418543338775635},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11373117566108704},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.10076585412025452},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.09720861911773682},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10021063","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10021063","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":[{"id":"https://metadata.un.org/sdg/11","score":0.6899999976158142,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W2407991977","https://openalex.org/W2519091744","https://openalex.org/W2743617586","https://openalex.org/W2786827964","https://openalex.org/W2792764867","https://openalex.org/W2899771611","https://openalex.org/W2948517885","https://openalex.org/W2950361482","https://openalex.org/W2950757722","https://openalex.org/W2963166639","https://openalex.org/W2987821246","https://openalex.org/W2997116778","https://openalex.org/W3015959599","https://openalex.org/W3040266635","https://openalex.org/W3081497074","https://openalex.org/W3093787659","https://openalex.org/W3105931142","https://openalex.org/W3106543020","https://openalex.org/W3128634608","https://openalex.org/W3135550350","https://openalex.org/W3166888304","https://openalex.org/W3169450514","https://openalex.org/W3170937175","https://openalex.org/W3170981104","https://openalex.org/W3175818677","https://openalex.org/W3184127157","https://openalex.org/W3188779816","https://openalex.org/W3188872815","https://openalex.org/W3191802839","https://openalex.org/W3198381997","https://openalex.org/W3199473923","https://openalex.org/W3210313187","https://openalex.org/W4206185942","https://openalex.org/W4224316504","https://openalex.org/W4283318673","https://openalex.org/W4385245566","https://openalex.org/W6749825310","https://openalex.org/W6756040250","https://openalex.org/W6784869275","https://openalex.org/W6797155008","https://openalex.org/W6802061597"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W2406638334","https://openalex.org/W4390961098","https://openalex.org/W2183306018","https://openalex.org/W2549990292","https://openalex.org/W2345479200","https://openalex.org/W1991765889","https://openalex.org/W2951819827","https://openalex.org/W2849310602","https://openalex.org/W4387250752"],"abstract_inverted_index":{"Unsupervised":[0],"anomaly":[1,74],"detection":[2],"for":[3,19,71,120],"multivariate":[4],"time":[5,29,72],"series":[6,30,73],"has":[7,144],"been":[8],"an":[9],"active":[10],"area":[11],"of":[12,40,89,95],"research":[13],"due":[14],"to":[15,83,104,130],"its":[16],"enormous":[17],"potential":[18],"industrial":[20],"applications.":[21],"Existing":[22],"works":[23],"have":[24],"made":[25],"extraordinary":[26],"progress":[27],"in":[28],"representation,":[31],"reconstruction":[32],"and":[33,43,68,86,117],"forecasting.":[34],"However,":[35],"the":[36,44,55,110,146],"intricate":[37],"temporal":[38,85,96],"patterns":[39],"individual":[41],"signals":[42,48,111],"relationships":[45],"between":[46],"different":[47],"are":[49,112],"rarely":[50],"explicitly":[51],"considered,":[52],"which":[53],"limits":[54],"performance.":[56,148],"To":[57,91],"this":[58],"end,":[59],"we":[60,77,98,125],"propose":[61],"a":[62,79,100,127],"novel":[63],"approach":[64],"based":[65],"on":[66,136],"Transformer":[67],"signal":[69],"decomposition":[70],"detection.":[75],"Specifically,":[76],"provide":[78],"multi-view":[80],"embedding":[81],"method":[82,143],"capture":[84],"correlational":[87],"features":[88],"signals.":[90],"take":[92],"full":[93],"advantage":[94],"patterns,":[97],"design":[99],"frequency":[101],"attention":[102],"module":[103],"extract":[105],"periodic":[106],"oscillation":[107],"features.":[108],"Then,":[109],"decomposed":[113],"into":[114],"seasonal,":[115],"trend":[116],"remainder":[118],"components":[119],"further":[121],"representation":[122],"separately.":[123],"Additionally,":[124],"employ":[126],"optimization":[128],"strategy":[129],"improve":[131],"feature":[132],"representation.":[133],"Extensive":[134],"experiments":[135],"various":[137],"public":[138],"benchmarks":[139],"demonstrate":[140],"that":[141],"our":[142],"achieved":[145],"state-of-the-art":[147]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}