{"id":"https://openalex.org/W4411725982","doi":"https://doi.org/10.1109/iscas56072.2025.11043371","title":"Harnessing Forecast Uncertainty in Deep Learning for Time Series Anomaly Detection with Posterior Distribution Scoring","display_name":"Harnessing Forecast Uncertainty in Deep Learning for Time Series Anomaly Detection with Posterior Distribution Scoring","publication_year":2025,"publication_date":"2025-05-25","ids":{"openalex":"https://openalex.org/W4411725982","doi":"https://doi.org/10.1109/iscas56072.2025.11043371"},"language":"en","primary_location":{"id":"doi:10.1109/iscas56072.2025.11043371","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas56072.2025.11043371","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Symposium on Circuits and Systems (ISCAS)","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/A5092843978","display_name":"Van Kwan Zhi Koh","orcid":"https://orcid.org/0000-0002-6207-4692"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Van Kwan Zhi Koh","raw_affiliation_strings":["Nanyang Technological University,School of Electrical and Electronic Engineering,Singapore"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University,School of Electrical and Electronic Engineering,Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100339251","display_name":"Ye Li","orcid":"https://orcid.org/0000-0002-8785-6092"},"institutions":[{"id":"https://openalex.org/I1341483097","display_name":"Public Utilities Board","ror":"https://ror.org/04ecck436","country_code":"SG","type":"government","lineage":["https://openalex.org/I1341483097","https://openalex.org/I4210165108"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Ye Li","raw_affiliation_strings":["Xylem Water Solutions Singapore Pte Ltd,Singapore"],"affiliations":[{"raw_affiliation_string":"Xylem Water Solutions Singapore Pte Ltd,Singapore","institution_ids":["https://openalex.org/I1341483097"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031555067","display_name":"Ehsan Shafiee","orcid":null},"institutions":[{"id":"https://openalex.org/I1341483097","display_name":"Public Utilities Board","ror":"https://ror.org/04ecck436","country_code":"SG","type":"government","lineage":["https://openalex.org/I1341483097","https://openalex.org/I4210165108"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Ehsan Shafiee","raw_affiliation_strings":["Xylem Water Solutions Singapore Pte Ltd,Singapore"],"affiliations":[{"raw_affiliation_string":"Xylem Water Solutions Singapore Pte Ltd,Singapore","institution_ids":["https://openalex.org/I1341483097"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049506273","display_name":"Zhiping Lin","orcid":"https://orcid.org/0000-0002-1587-1226"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Zhiping Lin","raw_affiliation_strings":["Nanyang Technological University,School of Electrical and Electronic Engineering,Singapore"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University,School of Electrical and Electronic Engineering,Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024709593","display_name":"Bihan Wen","orcid":"https://orcid.org/0000-0002-6874-6453"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Bihan Wen","raw_affiliation_strings":["Nanyang Technological University,School of Electrical and Electronic Engineering,Singapore"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University,School of Electrical and Electronic Engineering,Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5092843978"],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08143993,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9988999962806702,"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":0.9988999962806702,"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":0.9824000000953674,"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":0.9391000270843506,"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":0.7428485751152039},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.6334244012832642},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6057209372520447},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5844138264656067},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5759099721908569},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5355892181396484},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.43564078211784363},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42785656452178955},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.398624062538147},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36437922716140747},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.16217893362045288},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.11505496501922607}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7428485751152039},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6334244012832642},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6057209372520447},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5844138264656067},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5759099721908569},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5355892181396484},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.43564078211784363},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42785656452178955},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.398624062538147},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36437922716140747},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.16217893362045288},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.11505496501922607},{"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/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:10.1109/iscas56072.2025.11043371","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iscas56072.2025.11043371","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Symposium on Circuits and Systems (ISCAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.6899999976158142}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2296719434","https://openalex.org/W2407991977","https://openalex.org/W2786827964","https://openalex.org/W2950361482","https://openalex.org/W2963166639","https://openalex.org/W2980994438","https://openalex.org/W3089610451","https://openalex.org/W3128634608","https://openalex.org/W3169450514","https://openalex.org/W3170981104","https://openalex.org/W3177318507","https://openalex.org/W4200496287","https://openalex.org/W4247690662","https://openalex.org/W4288057688","https://openalex.org/W4308080033","https://openalex.org/W4376870121","https://openalex.org/W4384947542","https://openalex.org/W4385245566","https://openalex.org/W4386128207","https://openalex.org/W4386597332","https://openalex.org/W6720514713","https://openalex.org/W6754779804","https://openalex.org/W6766978945","https://openalex.org/W6784869275","https://openalex.org/W6802061597","https://openalex.org/W6858342418","https://openalex.org/W6859306471"],"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":[1,15,50,113],"anomaly":[2,58,105,144],"detection":[3],"tools":[4],"(TSAD)":[5],"are":[6,148],"widely":[7],"applicable":[8],"across":[9,175],"industries,":[10],"such":[11],"as":[12],"monitoring":[13],"time":[14,49,77,112],"data":[16],"of":[17,64,138,172],"water":[18],"pipeline":[19],"pressure,":[20],"network":[21],"traffic":[22],"activities,":[23],"and":[24,35,52,134],"hardware":[25],"telemetry.":[26],"The":[27,160],"primary":[28],"objective":[29,63],"is":[30,48,68],"to":[31,38,69,74,82,90,116,130,142,155],"identify":[32],"anomalous":[33,95,158],"segments":[34],"alert":[36],"users":[37],"potential":[39],"issues":[40],"before":[41],"any":[42],"consequences.":[43],"A":[44],"closely":[45],"related":[46],"tool":[47],"forecasting,":[51],"some":[53],"practitioners":[54],"leverage":[55],"it":[56,72,80],"for":[57,120],"detection.":[59],"As":[60],"the":[61,76,85,103,125,136,139,157,167],"main":[62],"a":[65,100],"forecasting":[66,86,114,126],"model":[67,91,165],"minimize":[70],"errors,":[71],"tends":[73],"over-fit":[75],"series,":[78],"making":[79],"challenging":[81],"distinguish":[83],"whether":[84],"errors":[87],"occur":[88],"due":[89],"limitations":[92],"or":[93],"true":[94],"segments.":[96,159],"This":[97],"paper":[98],"introduces":[99],"method":[101],"called":[102],"posterior":[104,140],"scoring":[106],"criterion,":[107],"which":[108],"uses":[109],"deep":[110],"learning":[111],"models":[115],"estimate":[117,131],"forecast":[118,132],"uncertainties":[119],"TSAD.":[121],"We":[122],"propose":[123],"replacing":[124],"model\u2019s":[127],"output":[128],"layer":[129],"distributions":[133],"compute":[135],"probability":[137],"distribution":[141],"attain":[143],"scores.":[145],"These":[146],"scores":[147],"processed":[149],"through":[150],"an":[151],"automated":[152],"threshold":[153],"criterion":[154],"classify":[156],"experiments":[161],"have":[162],"demonstrated":[163],"our":[164],"performs":[166],"best":[168],"in":[169],"four":[170],"out":[171],"five":[173],"datasets":[174],"seven":[176],"benchmark":[177],"models.":[178]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
