{"id":"https://openalex.org/W2805015610","doi":"https://doi.org/10.1080/01969722.2018.1448228","title":"Latent-Function-Based Residual Discrete Grey Model for Short-Term Demand Forecasting","display_name":"Latent-Function-Based Residual Discrete Grey Model for Short-Term Demand Forecasting","publication_year":2018,"publication_date":"2018-04-03","ids":{"openalex":"https://openalex.org/W2805015610","doi":"https://doi.org/10.1080/01969722.2018.1448228","mag":"2805015610"},"language":"en","primary_location":{"id":"doi:10.1080/01969722.2018.1448228","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969722.2018.1448228","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","raw_type":"journal-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/A5074770538","display_name":"Che-Jung Chang","orcid":"https://orcid.org/0000-0002-1145-0934"},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Che-Jung Chang","raw_affiliation_strings":["Department of Management Science and Engineering, Business School, Ningbo University, Ningbo City, Zhejiang Province, China"],"affiliations":[{"raw_affiliation_string":"Department of Management Science and Engineering, Business School, Ningbo University, Ningbo City, Zhejiang Province, China","institution_ids":["https://openalex.org/I109935558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111643724","display_name":"Wen-Li Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I108377105","display_name":"Tainan University of Technology","ror":"https://ror.org/03967fe87","country_code":"TW","type":"education","lineage":["https://openalex.org/I108377105"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Wen-Li Dai","raw_affiliation_strings":["Department of Information Management, Tainan University of Technology, Yongkang District, Tainan City, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Information Management, Tainan University of Technology, Yongkang District, Tainan City, Taiwan","institution_ids":["https://openalex.org/I108377105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028150954","display_name":"Der\u2010Chiang Li","orcid":"https://orcid.org/0000-0002-0887-1308"},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Der-Chiang Li","raw_affiliation_strings":["Department of Industrial and Information Management, National Cheng Kung University, East District, Tainan City, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Industrial and Information Management, National Cheng Kung University, East District, Tainan City, Taiwan","institution_ids":["https://openalex.org/I91807558"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101633061","display_name":"Chien-Chih Chen","orcid":"https://orcid.org/0000-0002-6041-3689"},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chien-Chih Chen","raw_affiliation_strings":["Department of Industrial and Information Management, National Cheng Kung University, East District, Tainan City, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Industrial and Information Management, National Cheng Kung University, East District, Tainan City, Taiwan","institution_ids":["https://openalex.org/I91807558"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5074770538"],"corresponding_institution_ids":["https://openalex.org/I109935558"],"apc_list":null,"apc_paid":null,"fwci":0.2118,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.58066786,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":"49","issue":"3","first_page":"170","last_page":"180"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12368","display_name":"Grey System Theory Applications","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T12368","display_name":"Grey System Theory Applications","score":0.9998000264167786,"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.9182000160217285,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/residual","display_name":"Residual","score":0.8202930688858032},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6984148025512695},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.6932983994483948},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5823620557785034},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.5490826964378357},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5485268235206604},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.5139369368553162},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4821317195892334},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39263495802879333},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34374672174453735},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.22768059372901917},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.22488921880722046},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1253817081451416}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.8202930688858032},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6984148025512695},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.6932983994483948},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5823620557785034},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.5490826964378357},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5485268235206604},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.5139369368553162},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4821317195892334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39263495802879333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34374672174453735},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.22768059372901917},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.22488921880722046},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1253817081451416},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/01969722.2018.1448228","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969722.2018.1448228","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6100000143051147,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W182476373","https://openalex.org/W1547417857","https://openalex.org/W1619438751","https://openalex.org/W1969191186","https://openalex.org/W1992665910","https://openalex.org/W1995221739","https://openalex.org/W1999478779","https://openalex.org/W2009993388","https://openalex.org/W2011095560","https://openalex.org/W2013182316","https://openalex.org/W2013377700","https://openalex.org/W2044445985","https://openalex.org/W2082985505","https://openalex.org/W2084349912","https://openalex.org/W2087120645","https://openalex.org/W2093689394","https://openalex.org/W2287571625","https://openalex.org/W2332105677","https://openalex.org/W2340169797","https://openalex.org/W3122796275","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W4239286941","https://openalex.org/W2088845016","https://openalex.org/W589102260","https://openalex.org/W1966421350","https://openalex.org/W2894173309","https://openalex.org/W4387932263","https://openalex.org/W2098962763","https://openalex.org/W2371065793","https://openalex.org/W2157746493","https://openalex.org/W1977222966"],"abstract_inverted_index":{"When":[0],"developing":[1,48,72],"a":[2,14,34,59,73,84,103,125,149,181,194,217],"production":[3],"plan,":[4],"accurate":[5,196],"forecasting":[6,93,126,169],"short-term":[7,94,221],"demand":[8,24,95,177,222],"is":[9,69,109,216],"challenging":[10],"for":[11,71,162],"managers":[12],"because":[13],"short":[15],"forecast":[16,30,55,165,197,225],"period":[17],"indicates":[18],"that":[19,108,193,209,219],"the":[20,44,65,89,119,131,140,146,154,159,164,168,176,190,202,210],"change":[21],"in":[22,213],"product":[23],"exhibits":[25],"an":[26,173],"unsteady":[27],"trend.":[28],"Therefore,":[29],"models":[31],"generated":[32],"using":[33,139,158,175,201,227],"large":[35],"amount":[36],"of":[37,62,148],"historical":[38],"observations":[39],"do":[40,52],"not":[41,53],"fully":[42],"explain":[43],"data":[45,178],"collected":[46],"on":[47,111],"patterns":[49],"and,":[50],"consequently,":[51],"robustly":[54],"outcomes.":[56],"However,":[57],"if":[58],"low":[60],"number":[61],"samples":[63,138],"featuring":[64],"most":[66],"recent":[67],"information":[68],"used":[70,130],"forecast,":[74],"management":[75],"efficiency":[76],"could":[77,82,198],"be":[78,199,224],"enhanced":[79],"and":[80,128,189],"enterprises":[81],"gain":[83],"competitive":[85],"advantage.":[86],"To":[87],"solve":[88],"problems":[90],"associated":[91],"with":[92],"when":[96],"small":[97,228],"datasets":[98],"are":[99],"available,":[100],"we":[101,116],"developed":[102,212],"residual":[104,113],"discrete":[105,120],"grey":[106,121],"model":[107,122,211],"based":[110],"modeling":[112,204],"analysis.":[114],"Specifically,":[115],"first":[117],"applied":[118],"to":[123,135,144,166,223],"create":[124],"model,":[127],"then":[129],"obtained":[132,157,179,200],"fitting":[133],"residuals":[134],"generate":[136],"training":[137],"Latent":[141],"Information":[142],"function":[143],"learn":[145],"topology":[147],"backpropagation":[150],"neural":[151],"network.":[152],"Finally,":[153],"predictive":[155],"errors":[156],"constructed":[160],"network":[161],"adjusting":[163],"enhance":[167],"performance.":[170],"We":[171],"conducted":[172],"experiment":[174],"from":[180],"thin":[182],"film":[183],"transistor":[184],"liquid":[185],"crystal":[186],"display":[187],"panel,":[188],"results":[191],"indicated":[192],"highly":[195],"proposed":[203],"procedure.":[205],"This":[206],"finding":[207],"suggests":[208],"this":[214],"study":[215],"tool":[218],"enables":[220],"accurately":[226],"datasets.":[229]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
