{"id":"https://openalex.org/W7134984946","doi":"https://doi.org/10.48550/arxiv.2603.09490","title":"Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection","display_name":"Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detection","publication_year":2026,"publication_date":"2026-03-10","ids":{"openalex":"https://openalex.org/W7134984946","doi":"https://doi.org/10.48550/arxiv.2603.09490"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.09490","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09490","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.09490","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041693211","display_name":"David Baumgartner","orcid":"https://orcid.org/0000-0002-0189-4718"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Baumgartner, David","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040301804","display_name":"Helge Langseth","orcid":"https://orcid.org/0000-0001-6324-6284"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Langseth, Helge","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128733109","display_name":"Kenth Eng\u00f8-Monsen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eng\u00f8-Monsen, Kenth","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5026537247","display_name":"Heri Ramampiaro","orcid":"https://orcid.org/0000-0003-0534-5924"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ramampiaro, Heri","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.95660001039505,"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":0.95660001039505,"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":0.026599999517202377,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.0024999999441206455,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7281000018119812},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.6740000247955322},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6172000169754028},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5475000143051147},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5087000131607056},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4368000030517578},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4025999903678894}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7281000018119812},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.6740000247955322},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6172000169754028},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5979999899864197},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5475000143051147},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5156000256538391},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5087000131607056},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4368000030517578},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4025999903678894},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.35199999809265137},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32010000944137573},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29919999837875366},{"id":"https://openalex.org/C74883015","wikidata":"https://www.wikidata.org/wiki/Q290467","display_name":"Autoregressive\u2013moving-average model","level":3,"score":0.28859999775886536},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C67226441","wikidata":"https://www.wikidata.org/wiki/Q1665389","display_name":"Robust statistics","level":3,"score":0.26919999718666077},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26109999418258667},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2526000142097473}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.09490","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09490","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.09490","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09490","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Gender equality","score":0.4199106991291046,"id":"https://metadata.un.org/sdg/5"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0,47],"paper":[1],"introduces":[2],"temporal-conditioned":[3],"normalizing":[4,28],"flows":[5,29],"(tcNF),":[6],"a":[7],"novel":[8],"framework":[9],"that":[10],"addresses":[11],"anomaly":[12,52],"detection":[13,53],"in":[14],"time":[15],"series":[16],"data":[17],"with":[18],"accurate":[19,41],"modeling":[20],"of":[21,44,80],"temporal":[22,37],"dependencies":[23],"and":[24,39,71,82,84,92],"uncertainty.":[25],"By":[26],"conditioning":[27],"on":[30,65],"previous":[31],"observations,":[32],"tcNF":[33,64],"effectively":[34],"captures":[35],"complex":[36],"dynamics":[38],"generates":[40],"probability":[42],"distributions":[43],"expected":[45],"behavior.":[46],"autoregressive":[48],"approach":[49],"enables":[50],"robust":[51],"by":[54],"identifying":[55],"low-probability":[56],"events":[57],"within":[58],"the":[59],"learned":[60],"distribution.":[61],"We":[62],"evaluate":[63],"diverse":[66],"datasets,":[67],"demonstrating":[68],"good":[69],"accuracy":[70],"robustness":[72],"compared":[73],"to":[74,89],"existing":[75],"methods.":[76],"A":[77],"comprehensive":[78],"analysis":[79],"strengths":[81],"limitations":[83],"open-source":[85],"code":[86],"is":[87],"provided":[88],"facilitate":[90],"reproducibility":[91],"future":[93],"research.":[94]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-12T00:00:00"}
