{"id":"https://openalex.org/W3015547700","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206939","title":"Anomaly Detection in Trajectory Data with Normalizing Flows","display_name":"Anomaly Detection in Trajectory Data with Normalizing Flows","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3015547700","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206939","mag":"3015547700"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9206939","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206939","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2004.05958","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027591817","display_name":"Madson L. D. Dias","orcid":"https://orcid.org/0000-0001-7952-5755"},"institutions":[{"id":"https://openalex.org/I243754102","display_name":"Universidade Federal do Cear\u00e1","ror":"https://ror.org/03srtnf24","country_code":"BR","type":"education","lineage":["https://openalex.org/I243754102"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Madson L. D. Dias","raw_affiliation_strings":["Department of Computer Science, Federal University of Cear\u00e1, Fortaleza, Brazil","Federal University of Cear\u00e1,Department of Computer Science,Fortaleza,Brazil"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Federal University of Cear\u00e1, Fortaleza, Brazil","institution_ids":["https://openalex.org/I243754102"]},{"raw_affiliation_string":"Federal University of Cear\u00e1,Department of Computer Science,Fortaleza,Brazil","institution_ids":["https://openalex.org/I243754102"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009178588","display_name":"C\u00e9sar Mattos","orcid":null},"institutions":[{"id":"https://openalex.org/I243754102","display_name":"Universidade Federal do Cear\u00e1","ror":"https://ror.org/03srtnf24","country_code":"BR","type":"education","lineage":["https://openalex.org/I243754102"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Cesar Lincoln C. Mattos","raw_affiliation_strings":["Department of Computer Science, Federal University of Cear\u00e1, Fortaleza, Brazil","[Federal University of Ceara]"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Federal University of Cear\u00e1, Fortaleza, Brazil","institution_ids":["https://openalex.org/I243754102"]},{"raw_affiliation_string":"[Federal University of Ceara]","institution_ids":["https://openalex.org/I243754102"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017570453","display_name":"Ticiana L. C. da Silva","orcid":null},"institutions":[{"id":"https://openalex.org/I243754102","display_name":"Universidade Federal do Cear\u00e1","ror":"https://ror.org/03srtnf24","country_code":"BR","type":"education","lineage":["https://openalex.org/I243754102"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Ticiana L. C. da Silva","raw_affiliation_strings":["Virtual UFC Institute, Federal University of Cear\u00e1, Fortaleza, Brazil","[Federal University of Ceara]"],"affiliations":[{"raw_affiliation_string":"Virtual UFC Institute, Federal University of Cear\u00e1, Fortaleza, Brazil","institution_ids":["https://openalex.org/I243754102"]},{"raw_affiliation_string":"[Federal University of Ceara]","institution_ids":["https://openalex.org/I243754102"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065118727","display_name":"Jos\u00e9 Ant\u00f4nio Fernandes de Mac\u00eado","orcid":"https://orcid.org/0000-0002-0661-2978"},"institutions":[{"id":"https://openalex.org/I243754102","display_name":"Universidade Federal do Cear\u00e1","ror":"https://ror.org/03srtnf24","country_code":"BR","type":"education","lineage":["https://openalex.org/I243754102"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Jose Antonio F. de Macedo","raw_affiliation_strings":["Department of Computer Science, Federal University of Cear\u00e1, Fortaleza, Brazil","[Federal University of Ceara]"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Federal University of Cear\u00e1, Fortaleza, Brazil","institution_ids":["https://openalex.org/I243754102"]},{"raw_affiliation_string":"[Federal University of Ceara]","institution_ids":["https://openalex.org/I243754102"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082608434","display_name":"Wellington C. P. Silva","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wellington C. P. Silva","raw_affiliation_strings":["National Department of Public Security, Federal District, Brazil","Federal District,National Department of Public Security,Brazil"],"affiliations":[{"raw_affiliation_string":"National Department of Public Security, Federal District, Brazil","institution_ids":[]},{"raw_affiliation_string":"Federal District,National Department of Public Security,Brazil","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5027591817"],"corresponding_institution_ids":["https://openalex.org/I243754102"],"apc_list":null,"apc_paid":null,"fwci":0.3977,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.67313355,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9976000189781189,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9898999929428101,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7979545593261719},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7647581100463867},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6666532158851624},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.6655701994895935},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5922472476959229},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5471975207328796},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5291992425918579},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5099395513534546},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.477277934551239},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46386972069740295},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.4420098066329956},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.42691856622695923},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41816988587379456},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3584386110305786},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21992969512939453},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14228719472885132}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7979545593261719},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7647581100463867},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6666532158851624},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.6655701994895935},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5922472476959229},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5471975207328796},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5291992425918579},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5099395513534546},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.477277934551239},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46386972069740295},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.4420098066329956},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.42691856622695923},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41816988587379456},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3584386110305786},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21992969512939453},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14228719472885132},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9206939","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206939","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2004.05958","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.05958","pdf_url":"https://arxiv.org/pdf/2004.05958","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:3015547700","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2004.05958.pdf","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2004.05958","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2004.05958","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:2004.05958","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2004.05958","pdf_url":"https://arxiv.org/pdf/2004.05958","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3015547700.pdf","grobid_xml":"https://content.openalex.org/works/W3015547700.grobid-xml"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W1543388142","https://openalex.org/W1544435011","https://openalex.org/W1556024794","https://openalex.org/W1626398438","https://openalex.org/W1866230956","https://openalex.org/W2026493302","https://openalex.org/W2046144220","https://openalex.org/W2049633694","https://openalex.org/W2101778102","https://openalex.org/W2115627867","https://openalex.org/W2126194848","https://openalex.org/W2136317921","https://openalex.org/W2140251882","https://openalex.org/W2142412278","https://openalex.org/W2144182447","https://openalex.org/W2159089489","https://openalex.org/W2268212270","https://openalex.org/W2338990760","https://openalex.org/W2431962807","https://openalex.org/W2613480438","https://openalex.org/W2787049730","https://openalex.org/W2792952081","https://openalex.org/W2937385938","https://openalex.org/W2950904108","https://openalex.org/W2962695743","https://openalex.org/W2963047245","https://openalex.org/W2963090522","https://openalex.org/W2963139417","https://openalex.org/W2963568900","https://openalex.org/W2964343746","https://openalex.org/W2970898247","https://openalex.org/W2988195122","https://openalex.org/W2990226253","https://openalex.org/W4239954780","https://openalex.org/W6610566761","https://openalex.org/W6632547301","https://openalex.org/W6633310491","https://openalex.org/W6636500457","https://openalex.org/W6639317949","https://openalex.org/W6714644935","https://openalex.org/W6718362372","https://openalex.org/W6738536549","https://openalex.org/W6748116059","https://openalex.org/W6750411654","https://openalex.org/W6752910514","https://openalex.org/W6761365013","https://openalex.org/W6763486065","https://openalex.org/W6763806838","https://openalex.org/W6770328910","https://openalex.org/W6770451272"],"related_works":["https://openalex.org/W3089549862","https://openalex.org/W3166104071","https://openalex.org/W2783817418","https://openalex.org/W3199795887","https://openalex.org/W1961596866","https://openalex.org/W3088240917","https://openalex.org/W3198866269","https://openalex.org/W2992670518","https://openalex.org/W2796461292","https://openalex.org/W3122253391","https://openalex.org/W3092023218","https://openalex.org/W3033543834","https://openalex.org/W2587920757","https://openalex.org/W2540438180","https://openalex.org/W2598593653","https://openalex.org/W3161195768","https://openalex.org/W2955642511","https://openalex.org/W2766365484","https://openalex.org/W2885414839","https://openalex.org/W3191388851"],"abstract_inverted_index":{"The":[0,155],"task":[1],"of":[2,18,22,49,60,96,102,167],"detecting":[3],"anomalous":[4],"data":[5,82,145],"patterns":[6],"is":[7],"as":[8,13,29],"important":[9,94],"in":[10,159],"practical":[11],"applications":[12],"challenging.":[14],"In":[15],"the":[16,103,108,160,165,168,171],"context":[17],"spatial":[19],"data,":[20],"recognition":[21],"unexpected":[23],"trajectories":[24],"brings":[25],"additional":[26],"difficulties,":[27],"such":[28,40],"high":[30],"dimensionality":[31],"and":[32,146],"varying":[33],"pattern":[34],"lengths.":[35,128],"We":[36,129],"aim":[37],"to":[38,57],"tackle":[39],"a":[41,44,73,112,119],"problem":[42],"from":[43,81],"probability":[45],"density":[46,79],"estimation":[47,80],"point":[48],"view,":[50],"since":[51],"it":[52,148],"provides":[53],"an":[54,67,93],"unsupervised":[55],"procedure":[56],"identify":[58],"out":[59],"distribution":[61],"samples.":[62],"More":[63],"specifically,":[64],"we":[65,106],"pursue":[66],"approach":[68],"based":[69],"on":[70],"normalizing":[71,97,138,176],"flows,":[72,98],"recent":[74],"framework":[75],"that":[76,173],"enables":[77,121],"complex":[78],"with":[83,126,137,149],"neural":[84],"networks.":[85],"Our":[86],"proposal":[87],"computes":[88],"exact":[89],"model":[90],"likelihood":[91],"values,":[92],"feature":[95],"for":[99],"each":[100],"segment":[101],"trajectory.":[104],"Then,":[105],"aggregate":[107],"segments'":[109],"likelihoods":[110],"into":[111],"single":[113],"coherent":[114],"trajectory":[115,144],"anomaly":[116,135,152],"score.":[117],"Such":[118],"strategy":[120],"handling":[122],"possibly":[123],"large":[124],"sequences":[125],"different":[127],"evaluate":[130],"our":[131],"methodology,":[132],"named":[133],"aggregated":[134],"detection":[136,153],"flows":[139],"(GRADINGS),":[140],"using":[141],"real":[142],"world":[143],"compare":[147],"more":[150],"traditional":[151],"techniques.":[154],"promising":[156],"results":[157],"obtained":[158],"performed":[161],"computational":[162],"experiments":[163],"indicate":[164],"feasibility":[166],"GRADINGS,":[169],"specially":[170],"variant":[172],"considers":[174],"autoregressive":[175],"flows.":[177]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-02-27T16:54:17.756197","created_date":"2025-10-10T00:00:00"}
