{"id":"https://openalex.org/W4401754299","doi":"https://doi.org/10.3389/fdata.2024.1422650","title":"Efficient use of binned data for imputing univariate time series data","display_name":"Efficient use of binned data for imputing univariate time series data","publication_year":2024,"publication_date":"2024-08-21","ids":{"openalex":"https://openalex.org/W4401754299","doi":"https://doi.org/10.3389/fdata.2024.1422650","pmid":"https://pubmed.ncbi.nlm.nih.gov/39234189"},"language":"en","primary_location":{"id":"doi:10.3389/fdata.2024.1422650","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2024.1422650","pdf_url":"https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1422650/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1422650/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092695569","display_name":"Jay Darji","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jay Darji","raw_affiliation_strings":["Rhenix Lifesciences, Hyderabad, Telangana, India"],"affiliations":[{"raw_affiliation_string":"Rhenix Lifesciences, Hyderabad, Telangana, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017398516","display_name":"Nupur Biswas","orcid":"https://orcid.org/0000-0002-8468-5208"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Nupur Biswas","raw_affiliation_strings":["CureScience, San Diego, CA, United States","Rhenix Lifesciences, Hyderabad, Telangana, India"],"affiliations":[{"raw_affiliation_string":"CureScience, San Diego, CA, United States","institution_ids":[]},{"raw_affiliation_string":"Rhenix Lifesciences, Hyderabad, Telangana, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075051623","display_name":"Vijay Padul","orcid":"https://orcid.org/0009-0003-4406-9827"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vijay Padul","raw_affiliation_strings":["Rhenix Lifesciences, Hyderabad, Telangana, India"],"affiliations":[{"raw_affiliation_string":"Rhenix Lifesciences, Hyderabad, Telangana, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067399357","display_name":"Jaya M. Gill","orcid":"https://orcid.org/0000-0003-3608-4392"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jaya Gill","raw_affiliation_strings":["CureScience, San Diego, CA, United States"],"affiliations":[{"raw_affiliation_string":"CureScience, San Diego, CA, United States","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063863193","display_name":"Santosh Kesari","orcid":"https://orcid.org/0000-0003-3772-6000"},"institutions":[{"id":"https://openalex.org/I4210104056","display_name":"Saint John's Health Center","ror":"https://ror.org/01gcc9p15","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I4210104056","https://openalex.org/I94396219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Santosh Kesari","raw_affiliation_strings":["Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, United States"],"affiliations":[{"raw_affiliation_string":"Department of Translational Neurosciences, Pacific Neuroscience Institute and Saint John's Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, United States","institution_ids":["https://openalex.org/I4210104056"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011869717","display_name":"Shashaanka Ashili","orcid":"https://orcid.org/0000-0001-6335-0954"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shashaanka Ashili","raw_affiliation_strings":["CureScience, San Diego, CA, United States"],"affiliations":[{"raw_affiliation_string":"CureScience, San Diego, CA, United States","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5017398516"],"corresponding_institution_ids":[],"apc_list":{"value":1150,"currency":"USD","value_usd":1150},"apc_paid":{"value":1150,"currency":"USD","value_usd":1150},"fwci":0.4158,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.66612423,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"7","issue":null,"first_page":"1422650","last_page":"1422650"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10144","display_name":"Blood Pressure and Hypertension Studies","score":0.9800999760627747,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10144","display_name":"Blood Pressure and Hypertension Studies","score":0.9800999760627747,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9782000184059143,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.97079998254776,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.9031872749328613},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.6448060870170593},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.6276475191116333},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5616966485977173},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5229079127311707},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5115083456039429},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4944477081298828},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.45550206303596497},{"id":"https://openalex.org/keywords/data-reduction","display_name":"Data reduction","score":0.4510851800441742},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.41959524154663086},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.32625824213027954},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26213106513023376},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.16456928849220276},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.0781269371509552}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.9031872749328613},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.6448060870170593},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.6276475191116333},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5616966485977173},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5229079127311707},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5115083456039429},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4944477081298828},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.45550206303596497},{"id":"https://openalex.org/C153914771","wikidata":"https://www.wikidata.org/wiki/Q5227343","display_name":"Data reduction","level":2,"score":0.4510851800441742},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.41959524154663086},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.32625824213027954},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26213106513023376},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.16456928849220276},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0781269371509552},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3389/fdata.2024.1422650","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2024.1422650","pdf_url":"https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1422650/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},{"id":"pmid:39234189","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/39234189","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in big data","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:11371617","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11371617","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11371617/pdf/fdata-07-1422650.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Front Big Data","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:78b0540490134b5082e56d107e9f943d","is_oa":true,"landing_page_url":"https://doaj.org/article/78b0540490134b5082e56d107e9f943d","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Frontiers in Big Data, Vol 7 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3389/fdata.2024.1422650","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2024.1422650","pdf_url":"https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1422650/pdf","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8700000047683716,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4401754299.pdf","grobid_xml":"https://content.openalex.org/works/W4401754299.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W139541242","https://openalex.org/W1976364950","https://openalex.org/W1986194034","https://openalex.org/W2023419687","https://openalex.org/W2047627251","https://openalex.org/W2064186732","https://openalex.org/W2096849238","https://openalex.org/W2101234009","https://openalex.org/W2125291718","https://openalex.org/W2146182633","https://openalex.org/W2410255620","https://openalex.org/W2581082906","https://openalex.org/W2969070156","https://openalex.org/W2979377123","https://openalex.org/W2999144726","https://openalex.org/W3006713198","https://openalex.org/W3044592382","https://openalex.org/W3126204265","https://openalex.org/W3196402958","https://openalex.org/W4205671364","https://openalex.org/W4206992813","https://openalex.org/W4226239514","https://openalex.org/W4238111653","https://openalex.org/W4247219237","https://openalex.org/W4318464356","https://openalex.org/W6675354045","https://openalex.org/W6733568678","https://openalex.org/W6856521528"],"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/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W3191816118"],"abstract_inverted_index":{"Time":[0],"series":[1,75],"data":[2,20,80,89,101,126,150,172,181,214,226,238],"are":[3,32],"recorded":[4],"in":[5,9,25,121,133],"various":[6],"sectors,":[7],"resulting":[8,24],"a":[10,119,217],"large":[11],"amount":[12],"of":[13,18,27,42,52,61,73,102,136,171,179,187,192,207,220,231],"data.":[14,29,65,84,165,198,244],"However,":[15],"the":[16,36,40,50,54,59,87,110,129,134,137,169,177,185,193,201,208,229],"continuity":[17],"these":[19,43],"is":[21,45],"often":[22],"interrupted,":[23],"periods":[26],"missing":[28,37,62,88,156,164,188,209],"Several":[30],"algorithms":[31,98],"used":[33],"to":[34,128],"impute":[35,216],"data,":[38,76,157,189,194,212,221],"and":[39,63,81,94,154,195,205,210,236],"performance":[41,106],"methods":[44],"widely":[46],"varied.":[47],"Apart":[48],"from":[49,228],"choice":[51],"algorithm,":[53],"effective":[55],"imputation":[56],"depends":[57,182],"on":[58,184,200],"nature":[60],"available":[64,211],"We":[66,85,117,141,166,174],"conducted":[67],"extensive":[68],"studies":[69],"using":[70,96,109,124,148],"different":[71,91,97,103],"types":[72],"time":[74,92],"specifically":[77],"heart":[78,224],"rate":[79,225],"power":[82,242],"consumption":[83,243],"generated":[86],"for":[90,151,162],"spans":[93],"imputed":[95],"with":[99,158],"binned":[100,125,149,180,213],"sizes.":[104],"The":[105],"was":[107,145],"evaluated":[108],"root":[111],"mean":[112],"square":[113],"error":[114],"(RMSE)":[115],"metric.":[116],"observed":[118,161,168],"reduction":[120,160],"RMSE":[122,144],"when":[123,147],"compared":[127],"entire":[130],"dataset,":[131],"particularly":[132],"case":[135],"expectation-maximization":[138],"(EM)":[139],"algorithm.":[140],"found":[142],"that":[143,176],"reduced":[146],"1-,":[152],"5-,":[153],"15-min":[155,163],"greater":[159],"also":[167],"effect":[170],"fluctuation.":[173],"conclude":[175],"usefulness":[178],"precisely":[183],"span":[186],"sampling":[190],"frequency":[191],"fluctuation":[196],"within":[197],"Depending":[199],"inherent":[202],"characteristics,":[203],"quality,":[204],"quantity":[206],"can":[215],"wide":[218],"variety":[219],"including":[222],"biological":[223],"derived":[227],"Internet":[230],"Things":[232],"(IoT)":[233],"device":[234],"smartwatch":[235],"non-biological":[237],"such":[239],"as":[240],"household":[241]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-14T06:41:57.775601","created_date":"2025-10-10T00:00:00"}
