{"id":"https://openalex.org/W4387869687","doi":"https://doi.org/10.1109/mlsp55844.2023.10285979","title":"Low-Count Time Series Anomaly Detection","display_name":"Low-Count Time Series Anomaly Detection","publication_year":2023,"publication_date":"2023-09-17","ids":{"openalex":"https://openalex.org/W4387869687","doi":"https://doi.org/10.1109/mlsp55844.2023.10285979"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp55844.2023.10285979","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp55844.2023.10285979","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},"type":"article","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/A5016126493","display_name":"Philipp Renz","orcid":"https://orcid.org/0000-0002-3323-7632"},"institutions":[{"id":"https://openalex.org/I121883995","display_name":"Johannes Kepler University of Linz","ror":"https://ror.org/052r2xn60","country_code":"AT","type":"education","lineage":["https://openalex.org/I121883995"]}],"countries":["AT"],"is_corresponding":true,"raw_author_name":"Philipp Renz","raw_affiliation_strings":["Johannes Kepler University Linz,Austria","Johannes Kepler University Linz, Austria"],"affiliations":[{"raw_affiliation_string":"Johannes Kepler University Linz,Austria","institution_ids":["https://openalex.org/I121883995"]},{"raw_affiliation_string":"Johannes Kepler University Linz, Austria","institution_ids":["https://openalex.org/I121883995"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035070214","display_name":"Kurt Cutajar","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123934","display_name":"Amazon (United Kingdom)","ror":"https://ror.org/02xey9634","country_code":"GB","type":"company","lineage":["https://openalex.org/I1311688040","https://openalex.org/I4210123934"]},{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Kurt Cutajar","raw_affiliation_strings":["Amazon Prime Video,UK","Amazon Prime Video, UK"],"affiliations":[{"raw_affiliation_string":"Amazon Prime Video,UK","institution_ids":["https://openalex.org/I1311688040"]},{"raw_affiliation_string":"Amazon Prime Video, UK","institution_ids":["https://openalex.org/I4210123934"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089125860","display_name":"Niall Twomey","orcid":"https://orcid.org/0000-0002-3225-2654"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]},{"id":"https://openalex.org/I4210123934","display_name":"Amazon (United Kingdom)","ror":"https://ror.org/02xey9634","country_code":"GB","type":"company","lineage":["https://openalex.org/I1311688040","https://openalex.org/I4210123934"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Niall Twomey","raw_affiliation_strings":["Amazon Prime Video,UK","Amazon Prime Video, UK"],"affiliations":[{"raw_affiliation_string":"Amazon Prime Video,UK","institution_ids":["https://openalex.org/I1311688040"]},{"raw_affiliation_string":"Amazon Prime Video, UK","institution_ids":["https://openalex.org/I4210123934"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052350500","display_name":"Gavin Cheung","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123934","display_name":"Amazon (United Kingdom)","ror":"https://ror.org/02xey9634","country_code":"GB","type":"company","lineage":["https://openalex.org/I1311688040","https://openalex.org/I4210123934"]},{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Gavin K. C. Cheung","raw_affiliation_strings":["Amazon Prime Video,UK","Amazon Prime Video, UK"],"affiliations":[{"raw_affiliation_string":"Amazon Prime Video,UK","institution_ids":["https://openalex.org/I1311688040"]},{"raw_affiliation_string":"Amazon Prime Video, UK","institution_ids":["https://openalex.org/I4210123934"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054149665","display_name":"Hanting Xie","orcid":null},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]},{"id":"https://openalex.org/I4210123934","display_name":"Amazon (United Kingdom)","ror":"https://ror.org/02xey9634","country_code":"GB","type":"company","lineage":["https://openalex.org/I1311688040","https://openalex.org/I4210123934"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Hanting Xie","raw_affiliation_strings":["Amazon Prime Video,UK","Amazon Prime Video, UK"],"affiliations":[{"raw_affiliation_string":"Amazon Prime Video,UK","institution_ids":["https://openalex.org/I1311688040"]},{"raw_affiliation_string":"Amazon Prime Video, UK","institution_ids":["https://openalex.org/I4210123934"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5016126493"],"corresponding_institution_ids":["https://openalex.org/I121883995"],"apc_list":null,"apc_paid":null,"fwci":0.1748,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.56941629,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9997000098228455,"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.984000027179718,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.8203868865966797},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6930395364761353},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6683701872825623},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.6172510385513306},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.6030066609382629},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6013455390930176},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5947990417480469},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5905520915985107},{"id":"https://openalex.org/keywords/count-data","display_name":"Count data","score":0.5107431411743164},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4997274875640869},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.48658430576324463},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3078262209892273},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.29884734749794006},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14317601919174194},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13447514176368713}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8203868865966797},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6930395364761353},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6683701872825623},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6172510385513306},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.6030066609382629},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6013455390930176},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5947990417480469},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5905520915985107},{"id":"https://openalex.org/C33643355","wikidata":"https://www.wikidata.org/wiki/Q5176731","display_name":"Count data","level":3,"score":0.5107431411743164},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4997274875640869},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.48658430576324463},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3078262209892273},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29884734749794006},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14317601919174194},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13447514176368713},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"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/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp55844.2023.10285979","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp55844.2023.10285979","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1530232915","https://openalex.org/W1959608418","https://openalex.org/W2296719434","https://openalex.org/W2584499795","https://openalex.org/W2623293810","https://openalex.org/W3010896178","https://openalex.org/W3042623101","https://openalex.org/W3091751937","https://openalex.org/W3201545571","https://openalex.org/W4226060502","https://openalex.org/W4283324222","https://openalex.org/W4287826930","https://openalex.org/W4288057688","https://openalex.org/W4313824242","https://openalex.org/W4394647109","https://openalex.org/W6640963894","https://openalex.org/W6780872688","https://openalex.org/W6810609903","https://openalex.org/W6864715598"],"related_works":["https://openalex.org/W2499612753","https://openalex.org/W3111802945","https://openalex.org/W2806741695","https://openalex.org/W2946096271","https://openalex.org/W2295423552","https://openalex.org/W1598471830","https://openalex.org/W3107369729","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160"],"abstract_inverted_index":{"Low-count":[0],"time":[1,29,54,91],"series":[2,55,92],"describe":[3],"sparse":[4],"or":[5],"intermittent":[6],"events,":[7],"which":[8],"are":[9,38,47],"prevalent":[10],"in":[11,71],"large-scale":[12],"online":[13],"platforms":[14],"that":[15],"capture":[16],"and":[17,41,63,67,101,117,146],"monitor":[18],"diverse":[19],"data":[20,156],"types.":[21],"Several":[22],"distinct":[23],"challenges":[24],"surface":[25],"when":[26],"modelling":[27],"low-count":[28,90],"series,":[30],"particularly":[31],"low":[32],"signal-to-noise":[33],"ratios":[34],"(when":[35,44],"anomaly":[36,56,134],"signatures":[37],"provably":[39],"undetectable),":[40],"non-uniform":[42],"performance":[43],"average":[45],"metrics":[46],"not":[48],"representative":[49],"of":[50,89,99,143],"local":[51],"behaviour).":[52],"The":[53,140],"detection":[57],"community":[58],"currently":[59],"lacks":[60],"explicit":[61],"tooling":[62],"processes":[64],"to":[65,122,131],"model":[66],"reliably":[68],"detect":[69],"anomalies":[70],"these":[72],"settings.":[73],"We":[74],"address":[75],"this":[76,124],"gap":[77],"by":[78],"introducing":[79],"a":[80,97,151],"novel":[81],"generative":[82],"procedure":[83],"for":[84,157],"creating":[85],"benchmark":[86],"datasets":[87],"comprising":[88],"with":[93,111],"anomalous":[94,118],"segments.":[95,119],"Via":[96],"mixture":[98],"theoretical":[100],"empirical":[102],"analysis,":[103],"our":[104,129,144],"work":[105],"explains":[106],"how":[107,133],"widely-used":[108],"algorithms":[109],"struggle":[110],"the":[112],"distribution":[113],"overlap":[114],"between":[115],"normal":[116],"In":[120],"order":[121],"mitigate":[123],"shortcoming,":[125],"we":[126],"then":[127],"leverage":[128],"findings":[130],"demonstrate":[132],"score":[135],"smoothing":[136],"consistently":[137],"improves":[138],"performance.":[139],"practical":[141],"utility":[142],"analysis":[145],"recommendation":[147],"is":[148],"validated":[149],"on":[150],"real-world":[152],"dataset":[153],"containing":[154],"sales":[155],"retail":[158],"stores.":[159]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}