{"id":"https://openalex.org/W7147633268","doi":"https://doi.org/10.48550/arxiv.2603.29183","title":"IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detection","display_name":"IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detection","publication_year":2026,"publication_date":"2026-03-31","ids":{"openalex":"https://openalex.org/W7147633268","doi":"https://doi.org/10.48550/arxiv.2603.29183"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.29183","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29183","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2603.29183","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132587484","display_name":"Xiaohui Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Xiaohui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124094648","display_name":"Yijie Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yijie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091676049","display_name":"Hongzuo Xu","orcid":"https://orcid.org/0000-0001-8074-1244"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Hongzuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060806053","display_name":"Weixuan Liang","orcid":"https://orcid.org/0000-0002-1868-5445"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Weixuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132657647","display_name":"Xiaoli Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xiaoli","raw_affiliation_strings":[],"raw_orcid":null,"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":[],"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":0.9810000061988831,"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":0.9810000061988831,"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":0.004800000227987766,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0027000000700354576,"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":0.8030999898910522},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6730999946594238},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6521999835968018},{"id":"https://openalex.org/keywords/replicate","display_name":"Replicate","score":0.5817999839782715},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5620999932289124},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5199000239372253},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4018999934196472}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8030999898910522},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6730999946594238},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6521999835968018},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5958999991416931},{"id":"https://openalex.org/C2781162219","wikidata":"https://www.wikidata.org/wiki/Q26250693","display_name":"Replicate","level":2,"score":0.5817999839782715},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5620999932289124},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5199000239372253},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5144000053405762},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44510000944137573},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.438400000333786},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4018999934196472},{"id":"https://openalex.org/C519536355","wikidata":"https://www.wikidata.org/wiki/Q21021151","display_name":"Repurposing","level":2,"score":0.3971000015735626},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3707999885082245},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.35249999165534973},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.26840001344680786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.29183","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29183","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.29183","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29183","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"Open-set":[0],"anomaly":[1,15,75,160],"detection":[2],"(OSAD)":[3],"is":[4,85,114,183],"an":[5,118],"emerging":[6],"paradigm":[7],"designed":[8],"to":[9,20,36,54,62,65,107,115,141],"utilize":[10],"limited":[11],"labeled":[12],"data":[13,60,84],"from":[14],"classes":[16],"seen":[17,23],"in":[18,47,57,71],"training":[19,83,129],"identify":[21],"both":[22],"and":[24,134,179],"unseen":[25,42,147],"anomalies":[26,39,148,158],"during":[27],"testing.":[28],"Current":[29],"approaches":[30],"rely":[31],"on":[32,131],"simple":[33],"augmentation":[34],"methods":[35,51],"generate":[37,142],"pseudo":[38],"that":[40,97,121,165],"replicate":[41],"anomalies.":[43,89],"Despite":[44],"being":[45],"promising":[46],"image":[48],"data,":[49],"these":[50,109,138],"are":[52,78],"found":[53],"be":[55],"ineffective":[56],"time":[58,103,150],"series":[59,104,151],"due":[61],"the":[63,82,125,132],"failure":[64],"preserve":[66],"its":[67],"sequential":[68],"nature,":[69],"resulting":[70],"trivial":[72],"or":[73],"unrealistic":[74],"patterns.":[76],"They":[77],"further":[79],"plagued":[80],"when":[81],"contaminated":[86],"with":[87],"unlabeled":[88],"This":[90],"work":[91],"introduces":[92],"$\\textbf{IMPACT}$,":[93],"a":[94],"novel":[95],"framework":[96],"leverages":[98],"$\\underline{\\textbf{i}}$nfluence":[99],"$\\underline{\\textbf{m}}$odeling":[100],"for":[101,149,159],"o$\\underline{\\textbf{p}}$en-set":[102],"$\\underline{\\textbf{a}}$nomaly":[105],"dete$\\underline{\\textbf{ct}}$ion,":[106],"tackle":[108],"challenges.":[110],"The":[111],"key":[112],"insight":[113],"$\\textbf{i)}$":[116],"learn":[117],"influence":[119,139],"function":[120],"can":[122],"accurately":[123],"estimate":[124],"impact":[126],"of":[127],"individual":[128],"samples":[130,155],"modeling,":[133],"then":[135],"$\\textbf{ii)}$":[136],"leverage":[137],"scores":[140],"semantically":[143],"divergent":[144],"yet":[145],"realistic":[146],"while":[152],"repurposing":[153],"high-influential":[154],"as":[156],"supervised":[157],"decontamination.":[161],"Extensive":[162],"experiments":[163],"show":[164],"IMPACT":[166],"significantly":[167],"outperforms":[168],"existing":[169],"state-of-the-art":[170],"methods,":[171],"showing":[172],"superior":[173],"accuracy":[174],"under":[175],"varying":[176],"OSAD":[177],"settings":[178],"contamination":[180],"rates.":[181],"Code":[182],"available":[184],"at":[185],"https://github.com/mala-lab/IMPACT.":[186]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-02T00:00:00"}
