{"id":"https://openalex.org/W3135594323","doi":"https://doi.org/10.1002/cpe.6156","title":"An unsupervised neural network approach for imputation of missing values in univariate time series data","display_name":"An unsupervised neural network approach for imputation of missing values in univariate time series data","publication_year":2021,"publication_date":"2021-02-24","ids":{"openalex":"https://openalex.org/W3135594323","doi":"https://doi.org/10.1002/cpe.6156","mag":"3135594323"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1002/cpe.6156","pdf_url":null,"source":{"id":"https://openalex.org/S11065456","display_name":"Concurrency and Computation Practice and Experience","issn_l":"1532-0626","issn":["1532-0626","1532-0634"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"journal-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/A5022010331","display_name":"S. Nickolas","orcid":"https://orcid.org/0000-0002-0703-3839"},"institutions":[{"id":"https://openalex.org/I122964287","display_name":"National Institute of Technology Tiruchirappalli","ror":"https://ror.org/047x65e68","country_code":"IN","type":"education","lineage":["https://openalex.org/I122964287"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Nickolas Savarimuthu","raw_affiliation_strings":["Department of Computer Applications, National Institute of Technology, Tiruchirappalli, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Applications, National Institute of Technology, Tiruchirappalli, India","institution_ids":["https://openalex.org/I122964287"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041090297","display_name":"K. Shobha","orcid":null},"institutions":[{"id":"https://openalex.org/I122964287","display_name":"National Institute of Technology Tiruchirappalli","ror":"https://ror.org/047x65e68","country_code":"IN","type":"education","lineage":["https://openalex.org/I122964287"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Shobha Karesiddaiah","raw_affiliation_strings":["Department of Computer Applications, National Institute of Technology, Tiruchirappalli, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Applications, National Institute of Technology, Tiruchirappalli, India","institution_ids":["https://openalex.org/I122964287"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5041090297"],"corresponding_institution_ids":["https://openalex.org/I122964287"],"apc_list":{"value":4740,"currency":"USD","value_usd":4740,"provenance":"doaj"},"apc_paid":null,"fwci":1.663,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.999741,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":90},"biblio":{"volume":"33","issue":"9","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9951,"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":0.9951,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9796,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9746,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.85735995},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.8019972},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4641586}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.8641189},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.85735995},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.8019972},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5839758},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.57649076},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5363314},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.48226267},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.48144144},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.46775556},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4641586},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.447217},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41613752},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2711426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2697913}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1002/cpe.6156","pdf_url":null,"source":{"id":"https://openalex.org/S11065456","display_name":"Concurrency and Computation Practice and Experience","issn_l":"1532-0626","issn":["1532-0626","1532-0634"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.55,"display_name":"Industry, innovation and infrastructure"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":34,"referenced_works":["https://openalex.org/W1537066827","https://openalex.org/W1730150177","https://openalex.org/W1964391272","https://openalex.org/W1973315244","https://openalex.org/W1988904801","https://openalex.org/W1993220086","https://openalex.org/W2005149470","https://openalex.org/W2006761437","https://openalex.org/W2024121617","https://openalex.org/W2032441932","https://openalex.org/W2032594557","https://openalex.org/W2035025883","https://openalex.org/W2049633694","https://openalex.org/W2066046820","https://openalex.org/W2078965693","https://openalex.org/W2081050569","https://openalex.org/W2111700774","https://openalex.org/W2127550630","https://openalex.org/W2134843796","https://openalex.org/W2164522996","https://openalex.org/W2170435492","https://openalex.org/W2171118759","https://openalex.org/W2556237644","https://openalex.org/W2732236724","https://openalex.org/W2766098680","https://openalex.org/W2775056555","https://openalex.org/W2801828376","https://openalex.org/W2892452712","https://openalex.org/W2926030578","https://openalex.org/W3092602794","https://openalex.org/W3105296632","https://openalex.org/W4230876453","https://openalex.org/W4399495656","https://openalex.org/W575847903"],"related_works":["https://openalex.org/W4381149614","https://openalex.org/W4377565282","https://openalex.org/W3166949638","https://openalex.org/W3157093822","https://openalex.org/W3113847083","https://openalex.org/W3021292873","https://openalex.org/W2967771611","https://openalex.org/W2890686416","https://openalex.org/W2886732604","https://openalex.org/W2234472895"],"abstract_inverted_index":{"Summary":[0],"Handling":[1],"missing":[2,94,117],"values":[3,170],"in":[4,12,32,39,179],"time":[5,34,41,64,174],"series":[6,35,42,65,175],"data":[7,21,43,104,108,128,176],"plays":[8],"a":[9,82],"key":[10],"role":[11],"predicting":[13],"and":[14,18,72,111,122,149,154],"forecasting,":[15],"as":[16,145],"complete":[17],"clean":[19],"historical":[20],"help":[22],"to":[23,48,55,171],"achieve":[24],"higher":[25],"accuracy.":[26],"Numerous":[27],"research":[28],"works":[29],"are":[30],"present":[31],"multivariate":[33],"imputation,":[36],"but":[37],"imputation":[38,59,87,166,187],"univariate":[40,63],"is":[44,99,134],"least":[45],"considered":[46],"due":[47],"correlated":[49],"variables":[50],"unavailability.":[51],"This":[52],"article":[53],"aims":[54],"propose":[56],"an":[57,146],"iterative":[58],"algorithm":[60],"by":[61,114],"clustering":[62],"data,":[66],"considering":[67],"the":[68,76,107,127,137,164,172],"trend,":[69],"seasonality,":[70],"cyclical,":[71],"residue":[73],"features":[74],"of":[75],"data.":[77],"The":[78,96,130],"proposed":[79,97,131,165],"method":[80,98],"uses":[81],"similarity":[83],"based":[84],"nearest":[85],"neighbor":[86],"approach":[88],"on":[89,101],"each":[90],"clusters":[91],"for":[92],"filling":[93],"values.":[95],"evaluated":[100,135],"publicly":[102],"available":[103],"set":[105],"from":[106],"market":[109],"repository":[110,113],"UCI":[112],"randomly":[115],"simulating":[116],"patterns":[118],"under":[119],"low,":[120],"moderate,":[121],"high":[123],"missingness":[124],"rates":[125,182],"throughout":[126],"series.":[129],"method's":[132],"outcome":[133],"with":[136,140,184],"imputeTestbench":[138],"package":[139],"root":[141],"mean":[142],"squared":[143],"error":[144,147,181],"metric":[148],"validated":[150],"through":[151],"prediction":[152],"accuracy":[153],"concordance":[155],"correlation":[156],"coefficient":[157],"statistical":[158],"test.":[159],"Experimental":[160],"results":[161],"show":[162],"that":[163],"technique":[167],"produces":[168],"closer":[169],"original":[173],"set,":[177],"resulting":[178],"low":[180],"compared":[183],"other":[185],"existing":[186],"methods.":[188]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3135594323","counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":3}],"updated_date":"2025-01-03T09:38:35.557395","created_date":"2021-03-15"}