{"id":"https://openalex.org/W4394628780","doi":"https://doi.org/10.1109/cloudnet59005.2023.10490086","title":"Multivariate Time Series Anomaly Detection with Fourier Time Series Transformer","display_name":"Multivariate Time Series Anomaly Detection with Fourier Time Series Transformer","publication_year":2023,"publication_date":"2023-11-01","ids":{"openalex":"https://openalex.org/W4394628780","doi":"https://doi.org/10.1109/cloudnet59005.2023.10490086"},"language":"en","primary_location":{"id":"doi:10.1109/cloudnet59005.2023.10490086","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/cloudnet59005.2023.10490086","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 12th International Conference on Cloud Networking (CloudNet)","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/A5101713674","display_name":"Yufeng Ye","orcid":"https://orcid.org/0000-0002-6396-5681"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yufeng Ye","raw_affiliation_strings":["Guangzhou University,Guangzhou,China","Guangzhou University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou University,Guangzhou,China","institution_ids":["https://openalex.org/I37987034"]},{"raw_affiliation_string":"Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100918262","display_name":"Qichao He","orcid":null},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qichao He","raw_affiliation_strings":["Guangzhou University,Guangzhou,China","Guangzhou University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou University,Guangzhou,China","institution_ids":["https://openalex.org/I37987034"]},{"raw_affiliation_string":"Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100364041","display_name":"Peng Zhang","orcid":"https://orcid.org/0000-0001-7973-2746"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Zhang","raw_affiliation_strings":["Guangzhou University,Guangzhou,China","Guangzhou University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou University,Guangzhou,China","institution_ids":["https://openalex.org/I37987034"]},{"raw_affiliation_string":"Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055381291","display_name":"Jie Xiao","orcid":"https://orcid.org/0000-0001-6004-218X"},"institutions":[{"id":"https://openalex.org/I4210091192","display_name":"Zhejiang Zanyu Technology (China)","ror":"https://ror.org/00e58e871","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091192"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Xiao","raw_affiliation_strings":["Hangzhou Yugu Technology Co., Ltd,Hangzhou,China","Hangzhou Yugu Technology Co., Ltd, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Yugu Technology Co., Ltd,Hangzhou,China","institution_ids":["https://openalex.org/I4210091192"]},{"raw_affiliation_string":"Hangzhou Yugu Technology Co., Ltd, Hangzhou, China","institution_ids":["https://openalex.org/I4210091192"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032277491","display_name":"Zhao Li","orcid":"https://orcid.org/0000-0002-5056-0351"},"institutions":[{"id":"https://openalex.org/I4210091192","display_name":"Zhejiang Zanyu Technology (China)","ror":"https://ror.org/00e58e871","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210091192"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhao Li","raw_affiliation_strings":["Hangzhou Yugu Technology Co., Ltd,Hangzhou,China","Hangzhou Yugu Technology Co., Ltd, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Yugu Technology Co., Ltd,Hangzhou,China","institution_ids":["https://openalex.org/I4210091192"]},{"raw_affiliation_string":"Hangzhou Yugu Technology Co., Ltd, Hangzhou, China","institution_ids":["https://openalex.org/I4210091192"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3263,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.68088529,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"32","issue":null,"first_page":"381","last_page":"388"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9987000226974487,"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":0.9987000226974487,"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":0.9765999913215637,"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":0.9326000213623047,"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/series","display_name":"Series (stratigraphy)","score":0.6492375135421753},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5550119876861572},{"id":"https://openalex.org/keywords/fourier-series","display_name":"Fourier series","score":0.5329801440238953},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5011618137359619},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4742352366447449},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2622845470905304},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.19713559746742249},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.12344628572463989},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.07499703764915466},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.060191720724105835}],"concepts":[{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6492375135421753},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5550119876861572},{"id":"https://openalex.org/C207864730","wikidata":"https://www.wikidata.org/wiki/Q179467","display_name":"Fourier series","level":2,"score":0.5329801440238953},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5011618137359619},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4742352366447449},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2622845470905304},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.19713559746742249},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.12344628572463989},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.07499703764915466},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.060191720724105835},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cloudnet59005.2023.10490086","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/cloudnet59005.2023.10490086","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 12th International Conference on Cloud Networking (CloudNet)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.44999998807907104,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W93424348","https://openalex.org/W1959608418","https://openalex.org/W2080099070","https://openalex.org/W2118230478","https://openalex.org/W2743617586","https://openalex.org/W2785362611","https://openalex.org/W2786827964","https://openalex.org/W2792764867","https://openalex.org/W2950361482","https://openalex.org/W2954731415","https://openalex.org/W2963166639","https://openalex.org/W2964404039","https://openalex.org/W3014191625","https://openalex.org/W3081497074","https://openalex.org/W3098957257","https://openalex.org/W3106543020","https://openalex.org/W3169450514","https://openalex.org/W3170937175","https://openalex.org/W3170981104","https://openalex.org/W3177318507","https://openalex.org/W3199473923","https://openalex.org/W3204263062","https://openalex.org/W3212890323","https://openalex.org/W4281263494","https://openalex.org/W4283318673","https://openalex.org/W4306317275","https://openalex.org/W4385245566","https://openalex.org/W4385763767","https://openalex.org/W6640963894","https://openalex.org/W6748102297","https://openalex.org/W6749825310","https://openalex.org/W6751494907","https://openalex.org/W6764679822","https://openalex.org/W6797155008","https://openalex.org/W6802061597","https://openalex.org/W6810637551","https://openalex.org/W6889955440"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4242725041","https://openalex.org/W3158726648","https://openalex.org/W2090185172","https://openalex.org/W2622688551","https://openalex.org/W1550175370","https://openalex.org/W1990205660","https://openalex.org/W4387250752"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,33,177],"in":[2,10,27],"time":[3,21,44,61,96,101,124,154,164],"series":[4,22,45,62,102,125,155,165],"data":[5,23,63,126],"plays":[6],"a":[7,81,139],"key":[8],"role":[9],"automatic":[11],"industrial":[12],"operations.":[13],"Due":[14],"to":[15,122],"the":[16,25,39,48,92,106,113,117,148,161,175,180],"intricate":[17],"temporal":[18,40,114,149],"dependencies":[19],"within":[20,60],"and":[24,72,97,150],"difficulty":[26],"obtaining":[28],"labeled":[29],"data,":[30,46,156],"recent":[31],"anomaly":[32,103,166,176],"methods":[34],"have":[35],"primarily":[36],"focused":[37],"on":[38,170],"domain":[41,50,129,133],"features":[42,93,134],"of":[43,94,147,153,163,179],"neglecting":[47],"frequency":[49,74,98,128,132,151],"features.":[51,75],"However,":[52],"spectral":[53],"analysis":[54],"can":[55,158],"better":[56],"utilize":[57],"periodic":[58],"information":[59],"such":[64],"as":[65],"seasonal":[66],"patterns,":[67],"which":[68,90],"helps":[69],"capturing":[70],"multi-scale":[71],"multiple":[73],"In":[76],"this":[77],"paper,":[78],"we":[79],"present":[80],"Fourier":[82,118],"Time":[83],"Series":[84],"Transformer":[85],"model":[86],"(FTST":[87],"for":[88,100,111],"short),":[89],"combines":[91],"both":[95],"domains":[99,152],"detection.":[104,167],"Specifically,":[105],"attention":[107],"mechanism":[108],"is":[109,120],"utilized":[110],"modeling":[112],"domain,":[115],"while":[116],"Transform":[119],"employed":[121],"transform":[123],"into":[127],"data.":[130],"The":[131],"are":[135],"then":[136],"modeled":[137],"using":[138],"Temporal":[140],"Convolutional":[141],"Network.":[142],"By":[143],"making":[144],"full":[145],"use":[146],"FTST":[157],"significantly":[159],"enhance":[160],"performance":[162,178],"Experimental":[168],"results":[169],"popular":[171],"benchmark":[172],"datasets":[173],"demonstrate":[174],"proposed":[181],"method.":[182]},"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"}
