{"id":"https://openalex.org/W2281821379","doi":"https://doi.org/10.1145/2939672.2939789","title":"Semi-Markov Switching Vector Autoregressive Model-Based Anomaly Detection in Aviation Systems","display_name":"Semi-Markov Switching Vector Autoregressive Model-Based Anomaly Detection in Aviation Systems","publication_year":2016,"publication_date":"2016-08-08","ids":{"openalex":"https://openalex.org/W2281821379","doi":"https://doi.org/10.1145/2939672.2939789","mag":"2281821379"},"language":"en","primary_location":{"id":"doi:10.1145/2939672.2939789","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2939672.2939789","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5035670477","display_name":"Igor Melnyk","orcid":"https://orcid.org/0000-0003-0220-0615"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Igor Melnyk","raw_affiliation_strings":["University of Minnesota, Minneapolis, MN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Minnesota, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014459472","display_name":"Arindam Banerjee","orcid":"https://orcid.org/0000-0002-7856-5699"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arindam Banerjee","raw_affiliation_strings":["University of Minnesota, Minneapolis, MN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Minnesota, Minneapolis, MN, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003155939","display_name":"Bryan Matthews","orcid":"https://orcid.org/0000-0003-0391-314X"},"institutions":[{"id":"https://openalex.org/I1280536761","display_name":"Ames Research Center","ror":"https://ror.org/02acart68","country_code":"US","type":"facility","lineage":["https://openalex.org/I1280536761","https://openalex.org/I4210124779"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bryan Matthews","raw_affiliation_strings":["NASA Ames Research Center, Moffett Field, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NASA Ames Research Center, Moffett Field, CA, USA","institution_ids":["https://openalex.org/I1280536761"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000625363","display_name":"Nikunj C. Oza","orcid":"https://orcid.org/0000-0002-5987-1033"},"institutions":[{"id":"https://openalex.org/I1280536761","display_name":"Ames Research Center","ror":"https://ror.org/02acart68","country_code":"US","type":"facility","lineage":["https://openalex.org/I1280536761","https://openalex.org/I4210124779"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nikunj Oza","raw_affiliation_strings":["NASA Ames Research Center, Moffett Field, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NASA Ames Research Center, Moffett Field, CA, USA","institution_ids":["https://openalex.org/I1280536761"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.6885,"has_fulltext":false,"cited_by_count":48,"citation_normalized_percentile":{"value":0.95047133,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1065","last_page":"1074"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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":1.0,"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":0.9950000047683716,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9871000051498413,"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":0.8735762238502502},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.7400609850883484},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7369794249534607},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.5789243578910828},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5719020366668701},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5590728521347046},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5461928844451904},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.5430268049240112},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4570160210132599},{"id":"https://openalex.org/keywords/aviation","display_name":"Aviation","score":0.4427618980407715},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.4373820424079895},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.42384108901023865},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.4212639629840851},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3661060333251953},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3221166729927063},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13672661781311035},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09441864490509033},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.07672953605651855},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07434070110321045}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8735762238502502},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.7400609850883484},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7369794249534607},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.5789243578910828},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5719020366668701},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5590728521347046},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5461928844451904},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5430268049240112},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4570160210132599},{"id":"https://openalex.org/C74448152","wikidata":"https://www.wikidata.org/wiki/Q765633","display_name":"Aviation","level":2,"score":0.4427618980407715},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.4373820424079895},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.42384108901023865},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.4212639629840851},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3661060333251953},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3221166729927063},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13672661781311035},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09441864490509033},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.07672953605651855},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07434070110321045},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","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},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2939672.2939789","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2939672.2939789","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1211320294","display_name":null,"funder_award_id":"IIS-1029711","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6139495888","display_name":null,"funder_award_id":"IIS-0953274","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7102845301","display_name":null,"funder_award_id":"IIS-0916750","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306101","display_name":"National Aeronautics and Space Administration","ror":"https://ror.org/027ka1x80"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W178056938","https://openalex.org/W1485657187","https://openalex.org/W1590183771","https://openalex.org/W1892149550","https://openalex.org/W1965324089","https://openalex.org/W1993474918","https://openalex.org/W1996735943","https://openalex.org/W2013746897","https://openalex.org/W2018588330","https://openalex.org/W2032044874","https://openalex.org/W2037098035","https://openalex.org/W2049633694","https://openalex.org/W2056702017","https://openalex.org/W2068398134","https://openalex.org/W2074812030","https://openalex.org/W2102716594","https://openalex.org/W2121316155","https://openalex.org/W2124192284","https://openalex.org/W2132870739","https://openalex.org/W2140369911","https://openalex.org/W2156638102","https://openalex.org/W2157237396","https://openalex.org/W2161029186","https://openalex.org/W2318956598","https://openalex.org/W2322524800","https://openalex.org/W2436394933","https://openalex.org/W2798909945","https://openalex.org/W2947626232","https://openalex.org/W3003778769","https://openalex.org/W4206698392","https://openalex.org/W4242100818","https://openalex.org/W4302377426","https://openalex.org/W6629000798","https://openalex.org/W6641780247","https://openalex.org/W6680971561","https://openalex.org/W6750968397"],"related_works":["https://openalex.org/W2109632706","https://openalex.org/W2359438011","https://openalex.org/W2054867888","https://openalex.org/W2119088021","https://openalex.org/W2347283725","https://openalex.org/W4380568901","https://openalex.org/W2042251007","https://openalex.org/W2077287522","https://openalex.org/W2937385938","https://openalex.org/W2063729131"],"abstract_inverted_index":{"In":[0,39],"this":[1,40,70],"work":[2],"we":[3,72],"consider":[4],"the":[5,21,42,98,110,136,146],"problem":[6],"of":[7,89,114,142],"anomaly":[8,124],"detection":[9],"in":[10,57],"heterogeneous,":[11],"multivariate,":[12],"variable-length":[13],"time":[14,31],"series":[15,32],"datasets.":[16],"Our":[17],"focus":[18],"is":[19,44,91,106],"on":[20,94,129],"aviation":[22],"safety":[23,67],"domain,":[24],"where":[25],"data":[26,102],"objects":[27],"are":[28,33],"flights":[29],"and":[30,36,64,101,117,131],"sensor":[34],"readings":[35],"pilot":[37],"switches.":[38],"context":[41],"goal":[43],"to":[45,51,59,109,121],"detect":[46,139],"anomalous":[47],"flight":[48,79],"segments,":[49],"due":[50,108],"mechanical,":[52],"environmental,":[53],"or":[54],"human":[55],"factors":[56],"order":[58],"identifying":[60],"operationally":[61],"significant":[62],"events":[63],"highlight":[65],"potential":[66],"risks.":[68],"For":[69],"purpose,":[71],"propose":[73],"a":[74,81],"framework":[75,105,137],"which":[76],"represents":[77],"each":[78],"using":[80],"semi-Markov":[82],"switching":[83],"vector":[84],"autoregressive":[85],"(SMS-VAR)":[86],"model.":[87],"Detection":[88],"anomalies":[90,143],"then":[92],"based":[93],"measuring":[95],"dissimilarities":[96],"between":[97],"model's":[99],"prediction":[100],"observation.":[103],"The":[104],"scalable,":[107],"inherent":[111],"parallel":[112],"nature":[113],"most":[115],"computations,":[116],"can":[118,138],"be":[119],"used":[120],"perform":[122],"online":[123],"detection.":[125],"Extensive":[126],"experimental":[127],"results":[128],"simulated":[130],"real":[132],"datasets":[133],"illustrate":[134],"that":[135],"various":[140],"types":[141],"along":[144],"with":[145],"key":[147],"parameters":[148],"involved.":[149]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":10},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":5}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}