{"id":"https://openalex.org/W3138256474","doi":"https://doi.org/10.1109/bigdata50022.2020.9377909","title":"A Verifiable Imputation Analysis for Univariate Time Series and Enabling Package","display_name":"A Verifiable Imputation Analysis for Univariate Time Series and Enabling Package","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3138256474","doi":"https://doi.org/10.1109/bigdata50022.2020.9377909","mag":"3138256474"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9377909","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9377909","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","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/A5020632441","display_name":"Nianjun Zhou","orcid":"https://orcid.org/0000-0002-3473-6097"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Nianjun Zhou","raw_affiliation_strings":["IBM Thomas J. Watson Research Center, Yorktown Heights, NY"],"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center, Yorktown Heights, NY","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033934770","display_name":"Dhaval Patel","orcid":"https://orcid.org/0000-0002-5449-6975"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dhaval Patel","raw_affiliation_strings":["IBM Thomas J. Watson Research Center, Yorktown Heights, NY"],"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center, Yorktown Heights, NY","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102744844","display_name":"Arun Iyengar","orcid":"https://orcid.org/0000-0003-4679-1920"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Arun Iyengar","raw_affiliation_strings":["IBM Thomas J. Watson Research Center, Yorktown Heights, NY"],"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center, Yorktown Heights, NY","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044958272","display_name":"Shrey Shrivastava","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shrey Shrivastava","raw_affiliation_strings":["IBM Thomas J. Watson Research Center, Yorktown Heights, NY"],"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center, Yorktown Heights, NY","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039999123","display_name":"Anuradha Bhamidipaty","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anuradha Bhamidipaty","raw_affiliation_strings":["IBM Thomas J. Watson Research Center, Yorktown Heights, NY"],"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center, Yorktown Heights, NY","institution_ids":["https://openalex.org/I4210114115"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5020632441"],"corresponding_institution_ids":["https://openalex.org/I4210114115"],"apc_list":null,"apc_paid":null,"fwci":0.3031,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.57485188,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"6","issue":null,"first_page":"1764","last_page":"1773"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9983000159263611,"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"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9983000159263611,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9979000091552734,"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/T10317","display_name":"Advanced Database Systems and Queries","score":0.97079998254776,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/imputation","display_name":"Imputation (statistics)","score":0.8523643016815186},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.782029926776886},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.7720746994018555},{"id":"https://openalex.org/keywords/verifiable-secret-sharing","display_name":"Verifiable secret sharing","score":0.6420902013778687},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6408213376998901},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.6180010437965393},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.5665109157562256},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5446606874465942},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.5171000957489014},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.4711189270019531},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31130582094192505},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2928895056247711},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.19839823246002197},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1341821551322937},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.09741505980491638},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08915799856185913}],"concepts":[{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.8523643016815186},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.782029926776886},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.7720746994018555},{"id":"https://openalex.org/C85847156","wikidata":"https://www.wikidata.org/wiki/Q59015987","display_name":"Verifiable secret sharing","level":3,"score":0.6420902013778687},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6408213376998901},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.6180010437965393},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.5665109157562256},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5446606874465942},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.5171000957489014},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.4711189270019531},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31130582094192505},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2928895056247711},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.19839823246002197},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1341821551322937},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.09741505980491638},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08915799856185913},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9377909","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9377909","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4300000071525574,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W228914182","https://openalex.org/W260994251","https://openalex.org/W285471286","https://openalex.org/W612972800","https://openalex.org/W1995875910","https://openalex.org/W2018214637","https://openalex.org/W2019381370","https://openalex.org/W2032997274","https://openalex.org/W2044150334","https://openalex.org/W2099111195","https://openalex.org/W2117200996","https://openalex.org/W2517221375","https://openalex.org/W2775056555","https://openalex.org/W2891381594","https://openalex.org/W2892452712","https://openalex.org/W2911600237","https://openalex.org/W2964010366","https://openalex.org/W2997919412","https://openalex.org/W3006713198","https://openalex.org/W3016106855","https://openalex.org/W3125096521","https://openalex.org/W4231057675","https://openalex.org/W4297796805","https://openalex.org/W6692319373","https://openalex.org/W6726341914","https://openalex.org/W6772013027"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W2055961818","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3191816118","https://openalex.org/W3028371478","https://openalex.org/W2081476516"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,50,100,124,141,152,159],"verifiable":[4,102],"imputation":[5,19,103,128,157],"process":[6,104],"and":[7,17,27,55,91,95,131,151,166],"an":[8],"enabling":[9],"tool":[10,139],"for":[11],"univariate":[12],"time":[13],"series.":[14],"Common":[15],"ad-hoc":[16],"case-specific":[18],"are":[20,83],"not":[21],"enough":[22],"to":[23,48,60,105,122],"ensure":[24,66],"high":[25,125],"quality":[26,126,173],"effective":[28],"imputation.":[29],"We":[30,39,98,136,154],"adopt":[31],"the":[32,45,57,62,67,73,109,118,138,156],"similar":[33],"verification":[34],"logic":[35],"of":[36,52,69,75,87,108,127,169],"supervised":[37],"learning.":[38],"use":[40,56],"artificial":[41,88],"missing":[42,84,89],"sampling":[43],"as":[44,140],"test":[46],"set":[47,51],"estimate":[49],"imputers'":[53],"performances":[54,59],"estimated":[58],"select":[61],"best":[63],"imputer.":[64],"To":[65],"correctness":[68],"selection,":[70],"we":[71,114],"analyze":[72],"impact":[74],"various":[76],"factors":[77,82],"on":[78],"estimation":[79],"accuracy.":[80],"Those":[81],"rate,":[85],"size":[86],"data":[90,133],"patterns,":[92],"selected":[93],"imputers,":[94],"noise":[96],"level.":[97],"propose":[99],"two-step":[101],"integrate":[106],"all":[107],"steps.":[110],"With":[111],"this":[112],"process,":[113,161],"can":[115],"always":[116],"leverage":[117],"most":[119],"suitable":[120],"imputer":[121],"achieve":[123],"without":[129],"tedious":[130],"error-prone":[132],"cleaning":[134],"efforts.":[135],"implement":[137],"Python":[142],"package,":[143],"with":[144,147,172],"many":[145],"imputers":[146],"their":[148],"unique":[149],"capabilities":[150],"API.":[153],"automate":[155],"through":[158],"standard":[160],"which":[162],"returns":[163],"imputed":[164],"results":[165],"detailed":[167],"rationales":[168],"selection":[170],"along":[171],"metrics.":[174]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}