{"id":"https://openalex.org/W7131459572","doi":"https://doi.org/10.48550/arxiv.2602.19068","title":"TimeRadar: A Domain-Rotatable Foundation Model for Time Series Anomaly Detection","display_name":"TimeRadar: A Domain-Rotatable Foundation Model for Time Series Anomaly Detection","publication_year":2026,"publication_date":"2026-02-22","ids":{"openalex":"https://openalex.org/W7131459572","doi":"https://doi.org/10.48550/arxiv.2602.19068"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.19068","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.19068","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2602.19068","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126825694","display_name":"Hui He","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"He, Hui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001991004","display_name":"Hezhe Qiao","orcid":"https://orcid.org/0000-0003-3511-0528"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiao, Hezhe","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Chen, Yutong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yutong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126703827","display_name":"Kun Yi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yi, Kun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5039104219","display_name":"Guansong Pang","orcid":"https://orcid.org/0000-0002-9877-2716"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pang, Guansong","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5126825694"],"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":0.6568999886512756,"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":0.6568999886512756,"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":0.15189999341964722,"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.047200001776218414,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.705299973487854},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5996999740600586},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5795000195503235},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5651999711990356},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5651000142097473},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.48170000314712524},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.47519999742507935},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44519999623298645},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4350999891757965}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.705299973487854},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5996999740600586},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5795000195503235},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5651999711990356},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5651000142097473},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5424000024795532},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.48170000314712524},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.47519999742507935},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45840001106262207},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44519999623298645},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4350999891757965},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.423799991607666},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38589999079704285},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.36390000581741333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36000001430511475},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.35420000553131104},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3515999913215637},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.3228999972343445},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.3222000002861023},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.31709998846054077},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.31349998712539673},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30880001187324524},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29409998655319214},{"id":"https://openalex.org/C50965678","wikidata":"https://www.wikidata.org/wiki/Q2724302","display_name":"Abnormality","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.25769999623298645},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.25690001249313354}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.19068","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.19068","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":"doi:10.48550/arxiv.2602.19068","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.19068","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":"article"},"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":{"Current":[0],"time":[1,17,39,102,146,156,232],"series":[2,40,103,147,233],"foundation":[3],"models":[4],"(TSFMs)":[5],"primarily":[6],"focus":[7],"on":[8],"learning":[9],"prevalent":[10],"and":[11,115,157],"regular":[12,62,188],"patterns":[13,57,63,185],"within":[14],"a":[15,83,101,105,124,139,154,214],"predefined":[16],"or":[18],"frequency":[19,158],"domain":[20,86,159,173],"to":[21,46,87,137,143,148,198,220,229],"enable":[22],"supervised":[23],"downstream":[24,36],"tasks":[25],"(e.g.,":[26],"forecasting).":[27],"Consequently,":[28],"they":[29],"are":[30],"often":[31],"ineffective":[32],"for":[33,160,174],"inherently":[34],"unsupervised":[35],"tasks-such":[37],"as":[38],"anomaly":[41],"detection":[42],"(TSAD),":[43],"which":[44],"aims":[45],"identify":[47],"rare,":[48],"irregular":[49],"patterns.":[50],"This":[51,164],"limitation":[52],"arises":[53],"because":[54],"such":[55],"abnormal":[56,116,184],"can":[58,110],"closely":[59],"resemble":[60],"the":[61,67,113,145,149,182,187,207,222,226,236],"when":[64],"presented":[65],"in":[66,82,135,169,235],"same":[68],"time/frequency":[69],"domain.":[70,238],"To":[71,121,195],"address":[72],"this":[73,122],"issue,":[74],"we":[75,211],"introduce":[76,213],"TimeRadar,":[77],"an":[78,170],"innovative":[79],"TSFM":[80],"built":[81],"fractional":[84,107,141],"time-frequency":[85,108,172],"support":[88],"generalist":[89],"TSAD":[90],"across":[91,118,190],"diverse":[92],"unseen":[93,193],"datasets.":[94,120,194],"Our":[95],"key":[96],"insight":[97],"is":[98,133,203],"that":[99,202],"rotating":[100],"into":[104],"data-dependent":[106],"representation":[109],"adaptively":[111],"differentiate":[112],"normal":[114],"signals":[117],"different":[119],"end,":[123],"novel":[125],"component,":[126],"namely":[127],"Fractionally":[128],"modulated":[129],"Time-Frequency":[130],"Reconstruction":[131],"(FTFRecon),":[132],"proposed":[134],"TimeRadar":[136,197],"leverage":[138],"learnable":[140],"order":[142],"rotate":[144],"most":[150],"pronounced":[151],"angle":[152],"between":[153],"continuous":[155],"accurate":[161],"data":[162,167,176,209,234],"reconstruction.":[163],"provides":[165],"adaptive":[166],"reconstruction":[168],"optimal":[171],"each":[175],"input,":[177],"enabling":[178],"effective":[179],"differentiation":[180],"of":[181,225],"unbounded":[183],"from":[186],"ones":[189],"datasets,":[191],"including":[192],"allow":[196],"model":[199,221],"local":[200,223],"abnormality":[201],"not":[204],"captured":[205],"by":[206],"global":[208],"reconstruction,":[210],"further":[212],"Contextual":[215],"Deviation":[216],"Learning":[217],"(CDL)":[218],"component":[219],"deviation":[224],"input":[227],"relative":[228],"its":[230],"contextual":[231],"rotatable":[237]},"counts_by_year":[],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2026-02-26T00:00:00"}
