{"id":"https://openalex.org/W2739716254","doi":"https://doi.org/10.24963/ijcai.2017/445","title":"Stochastic Online Anomaly Analysis for Streaming Time Series","display_name":"Stochastic Online Anomaly Analysis for Streaming Time Series","publication_year":2017,"publication_date":"2017-07-28","ids":{"openalex":"https://openalex.org/W2739716254","doi":"https://doi.org/10.24963/ijcai.2017/445","mag":"2739716254"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2017/445","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2017/445","pdf_url":"https://www.ijcai.org/proceedings/2017/0445.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2017/0445.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068791753","display_name":"Zhao Xu","orcid":"https://orcid.org/0000-0003-4480-7394"},"institutions":[{"id":"https://openalex.org/I4210089015","display_name":"Sharp Laboratories of Europe (United Kingdom)","ror":"https://ror.org/0075mfb88","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210089015"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Zhao Xu","raw_affiliation_strings":["NEC Laboratories Europe"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NEC Laboratories Europe","institution_ids":["https://openalex.org/I4210089015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037636074","display_name":"Kristian Kersting","orcid":"https://orcid.org/0000-0002-2873-9152"},"institutions":[{"id":"https://openalex.org/I31512782","display_name":"Technische Universit\u00e4t Darmstadt","ror":"https://ror.org/05n911h24","country_code":"DE","type":"education","lineage":["https://openalex.org/I31512782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Kristian Kersting","raw_affiliation_strings":["Technical University of Darmstadt"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Darmstadt","institution_ids":["https://openalex.org/I31512782"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032694380","display_name":"Lorenzo von Ritter","orcid":null},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lorenzo von Ritter","raw_affiliation_strings":["Technical University of Munich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich","institution_ids":["https://openalex.org/I62916508"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.2719,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":{"value":0.90979568,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3189","last_page":"3195"},"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":0.9995999932289124,"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":0.9966999888420105,"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/computer-science","display_name":"Computer science","score":0.7443364858627319},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.717357337474823},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6805672645568848},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6467480659484863},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5524185299873352},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4908061921596527},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.48563137650489807},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.45769792795181274},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3566563129425049},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33913135528564453},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.1545521318912506},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09523749351501465}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7443364858627319},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.717357337474823},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6805672645568848},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6467480659484863},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5524185299873352},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4908061921596527},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.48563137650489807},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.45769792795181274},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3566563129425049},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33913135528564453},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.1545521318912506},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09523749351501465},{"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2017/445","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2017/445","pdf_url":"https://www.ijcai.org/proceedings/2017/0445.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2017/445","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2017/445","pdf_url":"https://www.ijcai.org/proceedings/2017/0445.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2739716254.pdf","grobid_xml":"https://content.openalex.org/works/W2739716254.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W10656104","https://openalex.org/W113895413","https://openalex.org/W386331423","https://openalex.org/W1442292319","https://openalex.org/W1482411441","https://openalex.org/W1788497923","https://openalex.org/W1859870454","https://openalex.org/W1982012380","https://openalex.org/W2026493302","https://openalex.org/W2045325640","https://openalex.org/W2095654324","https://openalex.org/W2101618025","https://openalex.org/W2115627867","https://openalex.org/W2122646361","https://openalex.org/W2139998722","https://openalex.org/W2146502635","https://openalex.org/W2152385515","https://openalex.org/W2157578436","https://openalex.org/W2166851633","https://openalex.org/W2338990760","https://openalex.org/W3208688400","https://openalex.org/W4211049957","https://openalex.org/W4237284205","https://openalex.org/W4294562888"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4377864969","https://openalex.org/W2972971679"],"abstract_inverted_index":{"Identifying":[0],"patterns":[1],"in":[2,13,71],"time":[3,26,73,106],"series":[4,27,107],"that":[5],"exhibit":[6],"anomalous":[7,111],"behavior":[8],"is":[9,38],"of":[10,103,121],"increasing":[11],"importance":[12],"many":[14],"domains,":[15],"such":[16,55],"as":[17],"financial":[18],"and":[19,32,45,80,108],"Web":[20],"data":[21,28],"analysis.":[22],"In":[23,57],"real":[24],"applications,":[25],"often":[29],"arrive":[30],"continuously,":[31],"usually":[33],"only":[34],"a":[35,78],"single":[36],"scan":[37],"allowed":[39],"through":[40],"the":[41,86,100,119],"data.":[42],"Batch":[43],"learning":[44,83],"retrospective":[46],"segmentation":[47],"methods":[48],"would":[49],"not":[50],"be":[51],"well":[52],"applicable":[53],"to":[54],"scenarios.":[56],"this":[58],"paper,":[59],"we":[60,76],"present":[61],"an":[62],"online":[63,82],"nonparametric":[64],"Bayesian":[65],"method":[66,96],"OLAD":[67,87],"for":[68,85],"anomaly":[69],"analysis":[70,114],"streaming":[72],"series.":[74],"Moreover,":[75],"develop":[77],"novel":[79],"efficient":[81],"approach":[84],"model":[88],"based":[89],"on":[90,115],"stochastic":[91],"gradient":[92],"descent.":[93],"The":[94],"proposed":[95],"can":[97],"effectively":[98],"learn":[99],"underlying":[101],"dynamics":[102],"anomaly-contaminated":[104],"heavy-tailed":[105],"identify":[109],"potential":[110],"events.":[112],"Empirical":[113],"real-world":[116],"datasets":[117],"demonstrates":[118],"effectiveness":[120],"our":[122],"method.":[123]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
