{"id":"https://openalex.org/W4409157816","doi":"https://doi.org/10.1145/3690624.3709266","title":"TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection","display_name":"TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection","publication_year":2025,"publication_date":"2025-04-04","ids":{"openalex":"https://openalex.org/W4409157816","doi":"https://doi.org/10.1145/3690624.3709266"},"language":"en","primary_location":{"id":"doi:10.1145/3690624.3709266","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3690624.3709266","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","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/A5021412121","display_name":"Mengxuan Li","orcid":"https://orcid.org/0000-0001-7278-7891"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengxuan Li","raw_affiliation_strings":["Zhejiang University, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0000-0001-7278-7891","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064795214","display_name":"Ke Liu","orcid":"https://orcid.org/0000-0002-1508-2657"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Liu","raw_affiliation_strings":["Zhejiang University, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0000-0002-1508-2657","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008473103","display_name":"Hongyang Chen","orcid":"https://orcid.org/0000-0002-7626-0162"},"institutions":[{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyang Chen","raw_affiliation_strings":["Zhejiang Lab, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0000-0002-7626-0162","affiliations":[{"raw_affiliation_string":"Zhejiang Lab, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I4210123185"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052757755","display_name":"Jiajun Bu","orcid":"https://orcid.org/0000-0002-1097-2044"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiajun Bu","raw_affiliation_strings":["Zhejiang University, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0000-0002-1097-2044","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058072839","display_name":"Hongwei Wang","orcid":"https://orcid.org/0000-0001-6118-6505"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongwei Wang","raw_affiliation_strings":["Zhejiang University, Hangzhou, China","Zhejiang University, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0000-0001-6118-6505","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"Zhejiang University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047118636","display_name":"Haishuai Wang","orcid":"https://orcid.org/0000-0003-1617-0920"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haishuai Wang","raw_affiliation_strings":["Zhejiang University, Hangzhou, China","Zhejiang University, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0000-0003-1617-0920","affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"Zhejiang University, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"671","last_page":"682"},"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":0.9990000128746033,"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":0.9386000037193298,"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":0.7053382396697998},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6794360876083374},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6189430952072144},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4889580011367798},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4415222704410553},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4125054180622101},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.20591506361961365},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.12300118803977966}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7053382396697998},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6794360876083374},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6189430952072144},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4889580011367798},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4415222704410553},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4125054180622101},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.20591506361961365},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.12300118803977966},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3690624.3709266","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3690624.3709266","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W2080099070","https://openalex.org/W2127979711","https://openalex.org/W2407991977","https://openalex.org/W2523635132","https://openalex.org/W2593048559","https://openalex.org/W2676890506","https://openalex.org/W2786827964","https://openalex.org/W2899283552","https://openalex.org/W2904753829","https://openalex.org/W2905361499","https://openalex.org/W2950361482","https://openalex.org/W2963166639","https://openalex.org/W2965433388","https://openalex.org/W3036843665","https://openalex.org/W3091751937","https://openalex.org/W3093787659","https://openalex.org/W3106543020","https://openalex.org/W3127271503","https://openalex.org/W3170981104","https://openalex.org/W3173539742","https://openalex.org/W3177318507","https://openalex.org/W3212890323","https://openalex.org/W3215418478","https://openalex.org/W4214535608","https://openalex.org/W4241602464","https://openalex.org/W4254182148","https://openalex.org/W4283318673","https://openalex.org/W4306645406","https://openalex.org/W4308670407","https://openalex.org/W4312241741","https://openalex.org/W4312750676","https://openalex.org/W4312796583","https://openalex.org/W4312933730","https://openalex.org/W4312978380","https://openalex.org/W4313186498","https://openalex.org/W4313887267","https://openalex.org/W4382203079","https://openalex.org/W4385562572","https://openalex.org/W4386066586","https://openalex.org/W4388841071","https://openalex.org/W4390190370","https://openalex.org/W4390874592","https://openalex.org/W4403791819","https://openalex.org/W4403792529","https://openalex.org/W6727311480"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2667207928","https://openalex.org/W2912112202","https://openalex.org/W4377864969","https://openalex.org/W3120251014"],"abstract_inverted_index":{"Time":[0],"series":[1,67,111,200],"anomaly":[2,36,68,151,201],"detection":[3,69,202],"aims":[4],"to":[5,46,80,85,108,122,149,165,178,205],"identify":[6],"unusual":[7],"patterns":[8],"in":[9,23,38,53,183],"data":[10,40,112],"or":[11],"deviations":[12],"from":[13],"systems'":[14],"expected":[15],"behavior.":[16],"The":[17],"reconstruction-based":[18,44,208],"methods":[19,45],"are":[20,212],"the":[21,34,54,86,124,132,137,141,180],"mainstream":[22],"this":[24,61,82],"task,":[25],"which":[26],"learn":[27,47,166],"point-wise":[28],"representation":[29,75],"via":[30],"unsupervised":[31],"learning.":[32],"However,":[33],"unlabeled":[35],"points":[37],"training":[39],"may":[41],"cause":[42],"these":[43],"and":[48,96,117,144,168,171,197],"reconstruct":[49],"anomalous":[50],"data,":[51],"resulting":[52],"challenge":[55],"of":[56,88,126,139,161],"capturing":[57,140],"normal":[58],"patterns.":[59],"In":[60,153],"paper,":[62],"we":[63,105,155],"propose":[64],"a":[65,114,119,130,158,173],"time":[66,110,199],"method":[70,135],"based":[71],"on":[72,100,194],"implicit":[73],"neural":[74],"(INR)":[76],"reconstruction,":[77],"named":[78],"TSINR,":[79],"address":[81],"challenge.":[83],"Due":[84],"property":[87],"spectral":[89],"bias,":[90],"TSINR":[91,134,189],"enables":[92],"prioritizing":[93],"low-frequency":[94],"signals":[95],"exhibiting":[97],"poorer":[98],"performance":[99,193],"high-frequency":[101],"abnormal":[102],"data.":[103,128,152],"Specifically,":[104],"adopt":[106],"INR":[107,125,162],"parameterize":[109],"as":[113],"continuous":[115,163],"function":[116,164],"employ":[118],"transformer-based":[120],"architecture":[121],"predict":[123],"given":[127],"As":[129],"result,":[131],"proposed":[133],"achieves":[136,190],"advantage":[138],"temporal":[142],"continuity":[143],"thus":[145],"is":[146],"more":[147],"sensitive":[148],"discontinuous":[150],"addition,":[154],"further":[156],"design":[157],"novel":[159],"form":[160],"inter-":[167],"intra-channel":[169],"information,":[170],"leverage":[172],"pre-trained":[174],"large":[175],"language":[176],"model":[177],"amplify":[179],"intense":[181],"fluctuations":[182],"anomalies.":[184],"Extensive":[185],"experiments":[186],"demonstrate":[187],"that":[188],"superior":[191],"overall":[192],"both":[195],"univariate":[196],"multivariate":[198],"benchmarks":[203],"compared":[204],"other":[206],"state-of-the-art":[207],"methods.":[209],"Our":[210],"codes":[211],"available":[213],"here.":[214]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":8}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}