{"id":"https://openalex.org/W4417077743","doi":"https://doi.org/10.48550/arxiv.2509.15033","title":"Beyond Marginals: Learning Joint Spatio-Temporal Patterns for Multivariate Anomaly Detection","display_name":"Beyond Marginals: Learning Joint Spatio-Temporal Patterns for Multivariate Anomaly Detection","publication_year":2025,"publication_date":"2025-09-18","ids":{"openalex":"https://openalex.org/W4417077743","doi":"https://doi.org/10.48550/arxiv.2509.15033"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2509.15033","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.15033","pdf_url":"https://arxiv.org/pdf/2509.15033","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2509.15033","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041343657","display_name":"Padmaksha Roy","orcid":"https://orcid.org/0000-0002-9571-1117"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Roy, Padmaksha","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013572009","display_name":"Almuatazbellah Boker","orcid":"https://orcid.org/0000-0002-9484-7266"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Boker, Almuatazbellah","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5073678278","display_name":"Lamine Mili","orcid":"https://orcid.org/0000-0001-6134-3945"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mili, Lamine","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5041343657"],"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":0.9495999813079834,"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":0.9495999813079834,"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":0.032999999821186066,"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":0.0013000000035390258,"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":0.7311000227928162},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.679099977016449},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5379999876022339},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5307999849319458},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.5218999981880188},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4823000133037567},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4821999967098236}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7311000227928162},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.679099977016449},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5874999761581421},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5814999938011169},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5379999876022339},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5307999849319458},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.5218999981880188},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4823000133037567},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4821999967098236},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.39169999957084656},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.36410000920295715},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.3222000002861023},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.3158000111579895},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.3127000033855438},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3091999888420105},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.3041999936103821},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30379998683929443},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2687999904155731},{"id":"https://openalex.org/C38180746","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate analysis","level":2,"score":0.25920000672340393}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2509.15033","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.15033","pdf_url":"https://arxiv.org/pdf/2509.15033","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2509.15033","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.15033","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2509.15033","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.15033","pdf_url":"https://arxiv.org/pdf/2509.15033","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,25,62],"this":[1,82],"paper,":[2],"we":[3,118],"aim":[4],"to":[5,71,108,113,145,150],"improve":[6],"multivariate":[7,20,26],"anomaly":[8,31,154],"detection":[9],"(AD)":[10],"by":[11,35,83],"modeling":[12,84,94],"the":[13,36,88,93,129],"\\textit{time-varying":[14],"non-linear":[15],"spatio-temporal":[16],"correlations}":[17],"found":[18],"in":[19,87,135],"time":[21,27,41,52,66],"series":[22,28,42,53,67],"data":[23],".":[24],"data,":[29],"an":[30],"may":[32],"be":[33,72],"indicated":[34],"simultaneous":[37],"deviation":[38],"of":[39,95],"interrelated":[40],"from":[43],"their":[44],"expected":[45],"collective":[46],"behavior,":[47],"even":[48],"when":[49],"no":[50],"individual":[51],"exhibits":[54],"a":[55,105,120,124,136,140],"clearly":[56],"abnormal":[57],"pattern":[58],"on":[59],"its":[60],"own.":[61],"many":[63],"existing":[64],"approaches,":[65],"variables":[68],"are":[69,132],"assumed":[70],"(conditionally)":[73],"independent,":[74],"which":[75],"oversimplifies":[76],"real-world":[77],"interactions.":[78],"Our":[79],"approach":[80],"addresses":[81],"joint":[85],"dependencies":[86],"latent":[89,137],"space":[90,138],"and":[91,100,112,123,128,153],"decoupling":[92],"\\textit{marginal":[96],"distributions,":[97],"temporal":[98,110,127],"dynamics,":[99],"inter-variable":[101],"dependencies}.":[102],"We":[103],"use":[104],"transformer":[106],"encoder":[107],"capture":[109],"patterns,":[111],"model":[114],"spatial":[115,130],"(inter-variable)":[116],"dependencies,":[117],"fit":[119],"multi-variate":[121],"likelihood":[122],"copula.":[125],"The":[126],"components":[131],"trained":[133],"jointly":[134],"using":[139],"self-supervised":[141],"contrastive":[142],"learning":[143],"objective":[144],"learn":[146],"meaningful":[147],"feature":[148],"representations":[149],"separate":[151],"normal":[152],"samples.":[155]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}
