{"id":"https://openalex.org/W4286801139","doi":"https://doi.org/10.1007/s11063-022-10950-2","title":"Cyclic Gate Recurrent Neural Networks for Time Series Data with Missing Values","display_name":"Cyclic Gate Recurrent Neural Networks for Time Series Data with Missing Values","publication_year":2022,"publication_date":"2022-07-24","ids":{"openalex":"https://openalex.org/W4286801139","doi":"https://doi.org/10.1007/s11063-022-10950-2"},"language":"en","primary_location":{"id":"doi:10.1007/s11063-022-10950-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11063-022-10950-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11063-022-10950-2.pdf","source":{"id":"https://openalex.org/S140962798","display_name":"Neural Processing Letters","issn_l":"1370-4621","issn":["1370-4621","1573-773X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Processing Letters","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11063-022-10950-2.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011346090","display_name":"Philip B. Weerakody","orcid":"https://orcid.org/0000-0001-5589-1726"},"institutions":[{"id":"https://openalex.org/I176790772","display_name":"Murdoch University","ror":"https://ror.org/00r4sry34","country_code":"AU","type":"education","lineage":["https://openalex.org/I176790772"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Philip B. Weerakody","raw_affiliation_strings":["Discipline of Information Technology, Murdoch University, Perth, WA, Australia"],"raw_orcid":"https://orcid.org/0000-0001-5589-1726","affiliations":[{"raw_affiliation_string":"Discipline of Information Technology, Murdoch University, Perth, WA, Australia","institution_ids":["https://openalex.org/I176790772"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062739845","display_name":"Kok Wai Wong","orcid":"https://orcid.org/0000-0001-8767-1031"},"institutions":[{"id":"https://openalex.org/I176790772","display_name":"Murdoch University","ror":"https://ror.org/00r4sry34","country_code":"AU","type":"education","lineage":["https://openalex.org/I176790772"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Kok Wai Wong","raw_affiliation_strings":["Discipline of Information Technology, Murdoch University, Perth, WA, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Discipline of Information Technology, Murdoch University, Perth, WA, Australia","institution_ids":["https://openalex.org/I176790772"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051413585","display_name":"Guanjin Wang","orcid":"https://orcid.org/0000-0002-5258-0532"},"institutions":[{"id":"https://openalex.org/I176790772","display_name":"Murdoch University","ror":"https://ror.org/00r4sry34","country_code":"AU","type":"education","lineage":["https://openalex.org/I176790772"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Guanjin Wang","raw_affiliation_strings":["Discipline of Information Technology, Murdoch University, Perth, WA, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Discipline of Information Technology, Murdoch University, Perth, WA, Australia","institution_ids":["https://openalex.org/I176790772"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5011346090"],"corresponding_institution_ids":["https://openalex.org/I176790772"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":3.7145,"has_fulltext":true,"cited_by_count":30,"citation_normalized_percentile":{"value":0.94000834,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"55","issue":"2","first_page":"1527","last_page":"1554"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9994000196456909,"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":0.9994000196456909,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9987000226974487,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9954000115394592,"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/missing-data","display_name":"Missing data","score":0.8318231105804443},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.80022132396698},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7196634411811829},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6505342721939087},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.6497863531112671},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6453452110290527},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5357747673988342},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5057294368743896},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4985513687133789},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4084670841693878},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3387477993965149}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.8318231105804443},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.80022132396698},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7196634411811829},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6505342721939087},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.6497863531112671},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6453452110290527},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5357747673988342},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5057294368743896},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4985513687133789},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4084670841693878},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3387477993965149}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s11063-022-10950-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11063-022-10950-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11063-022-10950-2.pdf","source":{"id":"https://openalex.org/S140962798","display_name":"Neural Processing Letters","issn_l":"1370-4621","issn":["1370-4621","1573-773X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Processing Letters","raw_type":"journal-article"},{"id":"pmh:oai:researchrepository.murdoch.edu.au:65586","is_oa":false,"landing_page_url":"https://researchrepository.murdoch.edu.au/id/eprint/65586/","pdf_url":null,"source":{"id":"https://openalex.org/S4306400274","display_name":"Murdoch Research Repository (Murdoch University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I176790772","host_organization_name":"Murdoch University","host_organization_lineage":["https://openalex.org/I176790772"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"  Weerakody, P.B., Wong, K.W. <https://researchrepository.murdoch.edu.au/view/author/Wong, Kevin (Kok Wai).html> and Wang, G. <https://researchrepository.murdoch.edu.au/view/author/Wang, Guanjin.html>ORCID: 0000-0002-5258-0532 <http://orcid.org/0000-0002-5258-0532>   (2022)  Cyclic gate recurrent neural networks for time series data with missing values.     Neural Processing Letters .    ","raw_type":"Journal Article"}],"best_oa_location":{"id":"doi:10.1007/s11063-022-10950-2","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11063-022-10950-2","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11063-022-10950-2.pdf","source":{"id":"https://openalex.org/S140962798","display_name":"Neural Processing Letters","issn_l":"1370-4621","issn":["1370-4621","1573-773X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Processing Letters","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320989","display_name":"Murdoch University","ror":"https://ror.org/00r4sry34"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4286801139.pdf","grobid_xml":"https://content.openalex.org/works/W4286801139.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W1893585201","https://openalex.org/W2000280255","https://openalex.org/W2064675550","https://openalex.org/W2095649738","https://openalex.org/W2100266340","https://openalex.org/W2157331557","https://openalex.org/W2162800060","https://openalex.org/W2255847468","https://openalex.org/W2293634267","https://openalex.org/W2313407065","https://openalex.org/W2319453305","https://openalex.org/W2372035825","https://openalex.org/W2508429489","https://openalex.org/W2592022686","https://openalex.org/W2604847698","https://openalex.org/W2613328025","https://openalex.org/W2742491462","https://openalex.org/W2744556021","https://openalex.org/W2789372996","https://openalex.org/W2873418536","https://openalex.org/W2926030578","https://openalex.org/W2926585089","https://openalex.org/W2964010366","https://openalex.org/W2964211630","https://openalex.org/W2966008801","https://openalex.org/W2972809290","https://openalex.org/W2990508289","https://openalex.org/W2997353686","https://openalex.org/W3046711798","https://openalex.org/W3080899326","https://openalex.org/W3133618741","https://openalex.org/W3135404049","https://openalex.org/W3186328757","https://openalex.org/W3208556199","https://openalex.org/W4220796488","https://openalex.org/W4237650824","https://openalex.org/W4249593981","https://openalex.org/W4280496611","https://openalex.org/W6604896550"],"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/W2581984549"],"abstract_inverted_index":{"Abstract":[0],"Gated":[1,25],"Recurrent":[2,198],"Neural":[3,199],"Networks":[4,200],"(RNNs)":[5],"such":[6],"as":[7,98,100],"LSTM":[8],"and":[9,59,126,165,192,244],"GRU":[10],"have":[11,27,89],"been":[12,90],"highly":[13],"effective":[14],"in":[15,21,57,70,109],"handling":[16],"sequential":[17,205],"time":[18,51,67,163,206],"series":[19,68,164,207],"data":[20,58,69,124,159,179],"recent":[22],"years.":[23],"Although":[24],"RNNs":[26],"an":[28],"inherent":[29],"ability":[30,81],"to":[31,48,53,82,119,130,155],"learn":[32],"complex":[33,95],"temporal":[34],"dynamics,":[35],"there":[36],"is":[37,183],"potential":[38],"for":[39,209],"further":[40],"enhancement":[41],"by":[42,92],"enabling":[43],"these":[44,114],"deep":[45],"learning":[46,102,242],"networks":[47,150],"directly":[49],"use":[50],"information":[52],"recognise":[54],"time-dependent":[55],"patterns":[56],"identify":[60,120,157],"important":[61,158],"segments":[62,125,160],"of":[63,123,177,188,219,247],"time.":[64],"Synonymous":[65],"with":[66,151,216],"real-world":[71],"applications":[72],"are":[73,128,201],"missing":[74,87,107,139,178,220],"values,":[75,221],"which":[76,104],"often":[77],"reduce":[78],"a":[79,162],"model\u2019s":[80],"perform":[83],"predictive":[84],"tasks.":[85],"Historically,":[86],"values":[88,108,133],"handled":[91],"simple":[93],"or":[94,134],"imputation":[96,132],"techniques":[97],"well":[99],"machine":[101,241],"models,":[103,237],"manage":[105],"the":[106,110,121,171,174,186,248],"prediction":[111],"layers.":[112],"However,":[113],"methods":[115],"do":[116],"not":[117],"attempt":[118],"significance":[122],"therefore":[127],"susceptible":[129],"poor":[131],"model":[135,181],"degradation":[136],"from":[137],"high":[138,217],"value":[140],"rates.":[141],"This":[142],"paper":[143],"develops":[144],"Cyclic":[145,196,222],"Gate":[146,197,223],"enhanced":[147,224],"recurrent":[148,234],"neural":[149,235],"learnt":[152],"waveform":[153],"parameters":[154],"automatically":[156],"within":[161],"neglect":[166],"unimportant":[167],"segments.":[168],"By":[169],"using":[170],"proposed":[172],"networks,":[173],"negative":[175],"impact":[176],"on":[180,203],"performance":[182,229],"mitigated":[184],"through":[185],"addition":[187],"customised":[189],"cyclic":[190],"opening":[191],"closing":[193],"gate":[194],"operations.":[195],"tested":[202],"several":[204],"datasets":[208,215],"classification":[210],"performance.":[211],"For":[212],"long":[213],"sequence":[214],"rates":[218],"RNN":[225,250],"models":[226],"achieve":[227],"higher":[228],"metrics":[230],"than":[231],"standard":[232],"gated":[233],"network":[236,240],"conventional":[238],"non-neural":[239],"algorithms":[243],"current":[245],"state":[246],"art":[249],"cell":[251],"variants.":[252]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":3}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}