{"id":"https://openalex.org/W4321786066","doi":"https://doi.org/10.1108/imds-08-2022-0468","title":"Using deep learning to interpolate the missing data in time-series for\u00a0credit risks along supply chain","display_name":"Using deep learning to interpolate the missing data in time-series for\u00a0credit risks along supply chain","publication_year":2023,"publication_date":"2023-02-24","ids":{"openalex":"https://openalex.org/W4321786066","doi":"https://doi.org/10.1108/imds-08-2022-0468"},"language":"en","primary_location":{"id":"doi:10.1108/imds-08-2022-0468","is_oa":false,"landing_page_url":"https://doi.org/10.1108/imds-08-2022-0468","pdf_url":null,"source":{"id":"https://openalex.org/S37320504","display_name":"Industrial Management & Data Systems","issn_l":"0263-5577","issn":["0263-5577","1758-5783"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Industrial Management &amp; Data Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://eprints.gla.ac.uk/view/author/64972.html>","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100751243","display_name":"Wenfeng Zhang","orcid":"https://orcid.org/0000-0002-9223-4220"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenfeng Zhang","raw_affiliation_strings":["School of General Education, Chongqing Polytechnic Institute, Chongqing, PR China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of General Education, Chongqing Polytechnic Institute, Chongqing, PR China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024010407","display_name":"Ming K. Lim","orcid":"https://orcid.org/0000-0003-0809-9431"},"institutions":[{"id":"https://openalex.org/I65644108","display_name":"Adam Smith Institute","ror":"https://ror.org/031tkkf47","country_code":"GB","type":"education","lineage":["https://openalex.org/I65644108"]},{"id":"https://openalex.org/I7882870","display_name":"University of Glasgow","ror":"https://ror.org/00vtgdb53","country_code":"GB","type":"education","lineage":["https://openalex.org/I7882870"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ming K. Lim","raw_affiliation_strings":["Adam Smith Business School, University of Glasgow, Glasgow, UK"],"raw_orcid":"https://orcid.org/0000-0003-0809-9431","affiliations":[{"raw_affiliation_string":"Adam Smith Business School, University of Glasgow, Glasgow, UK","institution_ids":["https://openalex.org/I65644108","https://openalex.org/I7882870"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049290836","display_name":"Mei Yang","orcid":"https://orcid.org/0000-0003-2201-4510"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mei Yang","raw_affiliation_strings":["Chongqing University, Chongqing, China"],"raw_orcid":"https://orcid.org/0000-0003-2201-4510","affiliations":[{"raw_affiliation_string":"Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090776873","display_name":"Xingzhi Li","orcid":null},"institutions":[{"id":"https://openalex.org/I63371133","display_name":"Chongqing Jiaotong University","ror":"https://ror.org/01t001k65","country_code":"CN","type":"education","lineage":["https://openalex.org/I63371133"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingzhi Li","raw_affiliation_strings":["Chongqing Jiaotong University, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chongqing Jiaotong University, Chongqing, China","institution_ids":["https://openalex.org/I63371133"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066843217","display_name":"Du Ni","orcid":"https://orcid.org/0000-0002-7070-5255"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Du Ni","raw_affiliation_strings":["School of Management, Nanjing University of Posts and Telecommunications, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0002-7070-5255","affiliations":[{"raw_affiliation_string":"School of Management, Nanjing University of Posts and Telecommunications, Nanjing, China","institution_ids":["https://openalex.org/I41198531"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.7402,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.81822943,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"123","issue":"5","first_page":"1401","last_page":"1417"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.965399980545044,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.965399980545044,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9606999754905701,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13706","display_name":"Management and Optimization Techniques","score":0.954800009727478,"subfield":{"id":"https://openalex.org/subfields/1408","display_name":"Strategy and Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.8644626140594482},{"id":"https://openalex.org/keywords/predictability","display_name":"Predictability","score":0.7605205774307251},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7035922408103943},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.6915144324302673},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5499913692474365},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5033456683158875},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48038965463638306},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4777100086212158},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4344301223754883},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4130532741546631},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40069854259490967},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1107478141784668},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10201388597488403}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.8644626140594482},{"id":"https://openalex.org/C197640229","wikidata":"https://www.wikidata.org/wiki/Q2534066","display_name":"Predictability","level":2,"score":0.7605205774307251},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7035922408103943},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.6915144324302673},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5499913692474365},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5033456683158875},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48038965463638306},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4777100086212158},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4344301223754883},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4130532741546631},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40069854259490967},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1107478141784668},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10201388597488403},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1108/imds-08-2022-0468","is_oa":false,"landing_page_url":"https://doi.org/10.1108/imds-08-2022-0468","pdf_url":null,"source":{"id":"https://openalex.org/S37320504","display_name":"Industrial Management & Data Systems","issn_l":"0263-5577","issn":["0263-5577","1758-5783"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Industrial Management &amp; Data Systems","raw_type":"journal-article"},{"id":"pmh:oai:eprints.gla.ac.uk:290393","is_oa":true,"landing_page_url":"https://eprints.gla.ac.uk/view/author/64972.html>","pdf_url":null,"source":{"id":"https://openalex.org/S4210235606","display_name":"ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam)","issn_l":"2622-8912","issn":["2622-8912","2622-8920"],"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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Articles"}],"best_oa_location":{"id":"pmh:oai:eprints.gla.ac.uk:290393","is_oa":true,"landing_page_url":"https://eprints.gla.ac.uk/view/author/64972.html>","pdf_url":null,"source":{"id":"https://openalex.org/S4210235606","display_name":"ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam)","issn_l":"2622-8912","issn":["2622-8912","2622-8920"],"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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Articles"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.6600000262260437,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W784579088","https://openalex.org/W2020908197","https://openalex.org/W2053529851","https://openalex.org/W2057053883","https://openalex.org/W2064675550","https://openalex.org/W2073170661","https://openalex.org/W2078774137","https://openalex.org/W2079735306","https://openalex.org/W2095628874","https://openalex.org/W2140878266","https://openalex.org/W2145036165","https://openalex.org/W2163150789","https://openalex.org/W2169908081","https://openalex.org/W2171642129","https://openalex.org/W2410255620","https://openalex.org/W2417161696","https://openalex.org/W2463618975","https://openalex.org/W2486271310","https://openalex.org/W2573071207","https://openalex.org/W2588589522","https://openalex.org/W2604269166","https://openalex.org/W2678892993","https://openalex.org/W2738654846","https://openalex.org/W2742491462","https://openalex.org/W2781143270","https://openalex.org/W2791364881","https://openalex.org/W2802240654","https://openalex.org/W2803946395","https://openalex.org/W2810984894","https://openalex.org/W2885804815","https://openalex.org/W2889230014","https://openalex.org/W2895269073","https://openalex.org/W2911543806","https://openalex.org/W2916538948","https://openalex.org/W2919115771","https://openalex.org/W2922379396","https://openalex.org/W2937502515","https://openalex.org/W2947411064","https://openalex.org/W2963360736","https://openalex.org/W2964153283","https://openalex.org/W2967394486","https://openalex.org/W2978309629","https://openalex.org/W2984062413","https://openalex.org/W2991580101","https://openalex.org/W2997353686","https://openalex.org/W3010957596","https://openalex.org/W3011632114","https://openalex.org/W3021124440","https://openalex.org/W3024764003","https://openalex.org/W3034563984","https://openalex.org/W3045637573","https://openalex.org/W3046045515","https://openalex.org/W3097027647","https://openalex.org/W3105270488","https://openalex.org/W3124636951","https://openalex.org/W3124918635","https://openalex.org/W3125067306","https://openalex.org/W3126204265","https://openalex.org/W3159013107","https://openalex.org/W3165098340","https://openalex.org/W3207923907","https://openalex.org/W3217461219","https://openalex.org/W3217526077","https://openalex.org/W4210398911","https://openalex.org/W4285355882","https://openalex.org/W4309103050","https://openalex.org/W4392791013"],"related_works":["https://openalex.org/W2726467123","https://openalex.org/W2064726690","https://openalex.org/W4254065731","https://openalex.org/W4252678288","https://openalex.org/W1607297154","https://openalex.org/W4210820789","https://openalex.org/W2913177154","https://openalex.org/W4237782192","https://openalex.org/W4235131201","https://openalex.org/W4232793539"],"abstract_inverted_index":{"Purpose":[0],"As":[1],"the":[2,13,24,36,39,54,59,63,71,74,79,82,89,93,111,118,129,141,156,161,172,175,178,185,188,195,202,205,236,243],"supply":[3,16],"chain":[4,17],"is":[5,209],"a":[6,114,218],"highly":[7,21],"integrated":[8],"infrastructure":[9],"in":[10,15,65,96,113,121,136,144,171,181,221,235],"modern":[11],"business,":[12],"risks":[14,226],"are":[18],"also":[19],"becoming":[20],"contagious":[22],"among":[23],"target":[25,219],"company.":[26],"This":[27,191],"motivates":[28],"researchers":[29],"to":[30,35,51,73],"continuously":[31],"add":[32],"new":[33,46],"features":[34,47],"datasets":[37,77,99,176],"for":[38,174],"credit":[40,225],"risk":[41],"prediction":[42],"(CRP).":[43],"However,":[44],"adding":[45],"can":[48,139,168,215,241],"easily":[49],"lead":[50],"missing":[52,102,119,142,157,179],"of":[53,76,81,92,128,164,204,232,238],"data.":[55],"Design/methodology/approach":[56],"Based":[57],"on":[58],"gaps":[60],"summarized":[61],"from":[62],"literature":[64],"CRP,":[66],"this":[67,86,137],"study":[68,87,138,192],"first":[69],"introduces":[70],"approaches":[72],"building":[75],"and":[78,104,109,150,154,199,212,227],"framing":[80],"algorithmic":[83,94,126,162],"models.":[84],"Then,":[85],"tests":[88],"interpolation":[90,112,186,197,208],"effects":[91,198],"model":[95,127,163],"three":[97],"artificial":[98],"with":[100,117,177],"different":[101],"rates":[103],"compares":[105],"its":[106],"predictability":[107,203],"before":[108],"after":[110,184,207],"real":[115],"dataset":[116,206],"data":[120,143,158,180],"irregular":[122,145,182],"time-series.":[123],"Findings":[124],"The":[125],"time-decayed":[130],"long":[131],"short-term":[132],"memory":[133],"(TD-LSTM)":[134],"proposed":[135],"monitor":[140],"time-series":[146,152,183],"by":[147,187],"capturing":[148],"more":[149],"better":[151],"information,":[153],"interpolating":[155],"efficiently.":[159],"Moreover,":[160],"Deep":[165],"Neural":[166],"Network":[167],"be":[169],"used":[170],"CRP":[173,214],"TD-LSTM.":[189],"Originality/value":[190],"fully":[193],"validates":[194],"TD-LSTM":[196],"demonstrates":[200],"that":[201],"improved.":[210],"Accurate":[211],"timely":[213],"undoubtedly":[216],"assist":[217],"company":[220,244],"avoiding":[222],"losses.":[223,246],"Identifying":[224],"taking":[228],"preventive":[229],"measures":[230],"ahead":[231],"time,":[233],"especially":[234],"case":[237],"public":[239],"emergencies,":[240],"help":[242],"minimize":[245]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
