{"id":"https://openalex.org/W7124982780","doi":"https://doi.org/10.1109/jiot.2026.3656390","title":"Masked Transformer With Mixed Features for Retail Demand Forecasting to Optimize IoT-Driven Supply Chain Management","display_name":"Masked Transformer With Mixed Features for Retail Demand Forecasting to Optimize IoT-Driven Supply Chain Management","publication_year":2026,"publication_date":"2026-01-20","ids":{"openalex":"https://openalex.org/W7124982780","doi":"https://doi.org/10.1109/jiot.2026.3656390"},"language":null,"primary_location":{"id":"doi:10.1109/jiot.2026.3656390","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3656390","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","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/A5123405417","display_name":"Junhua Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I186272606","display_name":"Guangdong University of Foreign Studies","ror":"https://ror.org/00fhc9y79","country_code":"CN","type":"education","lineage":["https://openalex.org/I186272606"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Junhua Wang","raw_affiliation_strings":["School of Computer Science, South China Business College, Guangdong University of Foreign Studies, Guangzhou, Guangdong, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, South China Business College, Guangdong University of Foreign Studies, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I186272606"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123392067","display_name":"Ouya Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I4400600917","display_name":"Guangzhou College of Commerce","ror":"https://ror.org/04f0j5d06","country_code":null,"type":"education","lineage":["https://openalex.org/I4400600917"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ouya Zhang","raw_affiliation_strings":["School of Information Technology and Engineering, Guangzhou College of Commerce, Guangzhou, Guangdong, China"],"affiliations":[{"raw_affiliation_string":"School of Information Technology and Engineering, Guangzhou College of Commerce, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I4400600917"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029658447","display_name":"Yongjun Qi","orcid":"https://orcid.org/0000-0002-7866-024X"},"institutions":[{"id":"https://openalex.org/I4210164009","display_name":"Guangdong Baiyun University","ror":"https://ror.org/04wmrj902","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210164009"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongjun Qi","raw_affiliation_strings":["School of Big Data and Computing, Guangdong Baiyun University, Guangzhou, Guangdong, China"],"affiliations":[{"raw_affiliation_string":"School of Big Data and Computing, Guangdong Baiyun University, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I4210164009"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hailin Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164009","display_name":"Guangdong Baiyun University","ror":"https://ror.org/04wmrj902","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210164009"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hailin Tang","raw_affiliation_strings":["School of Big Data and Computing, Guangdong Baiyun University, Guangzhou, Guangdong, China"],"affiliations":[{"raw_affiliation_string":"School of Big Data and Computing, Guangdong Baiyun University, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I4210164009"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053864418","display_name":"Jiang Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I186272606","display_name":"Guangdong University of Foreign Studies","ror":"https://ror.org/00fhc9y79","country_code":"CN","type":"education","lineage":["https://openalex.org/I186272606"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Jiang","raw_affiliation_strings":["School of Marxism, South China Business College, Guangdong University of Foreign Studies, Guangzhou, Guangdong, China"],"affiliations":[{"raw_affiliation_string":"School of Marxism, South China Business College, Guangdong University of Foreign Studies, Guangzhou, Guangdong, China","institution_ids":["https://openalex.org/I186272606"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088698036","display_name":"Zagarzusem Khurelbaatar","orcid":"https://orcid.org/0000-0001-7440-4364"},"institutions":[{"id":"https://openalex.org/I144917393","display_name":"Mongolian University of Science and Technology","ror":"https://ror.org/02shmve39","country_code":"MN","type":"education","lineage":["https://openalex.org/I144917393"]}],"countries":["MN"],"is_corresponding":false,"raw_author_name":"Zagarzusem Khurelbaatar","raw_affiliation_strings":["School of Information and Communication Technology, Mongolian University of Science and Technology, Ulaanbaatar, Mongolia"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Technology, Mongolian University of Science and Technology, Ulaanbaatar, Mongolia","institution_ids":["https://openalex.org/I144917393"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078253587","display_name":"Khuder Altangerel","orcid":"https://orcid.org/0000-0001-7697-546X"},"institutions":[{"id":"https://openalex.org/I144917393","display_name":"Mongolian University of Science and Technology","ror":"https://ror.org/02shmve39","country_code":"MN","type":"education","lineage":["https://openalex.org/I144917393"]}],"countries":["MN"],"is_corresponding":false,"raw_author_name":"Khuder Altangerel","raw_affiliation_strings":["School of Information and Communication Technology, Mongolian University of Science and Technology, Ulaanbaatar, Mongolia"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Technology, Mongolian University of Science and Technology, Ulaanbaatar, Mongolia","institution_ids":["https://openalex.org/I144917393"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5123405417"],"corresponding_institution_ids":["https://openalex.org/I186272606"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15292065,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"7","first_page":"12644","last_page":"12654"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9031000137329102,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9031000137329102,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.028699999675154686,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.01269999984651804,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/demand-forecasting","display_name":"Demand forecasting","score":0.7466999888420105},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6650000214576721},{"id":"https://openalex.org/keywords/supply-chain","display_name":"Supply chain","score":0.6556000113487244},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.5641000270843506},{"id":"https://openalex.org/keywords/supply-chain-management","display_name":"Supply chain management","score":0.519599974155426},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.5059999823570251},{"id":"https://openalex.org/keywords/demand-management","display_name":"Demand management","score":0.421099990606308},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4000000059604645},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39910000562667847}],"concepts":[{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.7466999888420105},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7343999743461609},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6650000214576721},{"id":"https://openalex.org/C108713360","wikidata":"https://www.wikidata.org/wiki/Q1824206","display_name":"Supply chain","level":2,"score":0.6556000113487244},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.5641000270843506},{"id":"https://openalex.org/C44104985","wikidata":"https://www.wikidata.org/wiki/Q492886","display_name":"Supply chain management","level":3,"score":0.519599974155426},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.5059999823570251},{"id":"https://openalex.org/C179366874","wikidata":"https://www.wikidata.org/wiki/Q1185130","display_name":"Demand management","level":2,"score":0.421099990606308},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4000000059604645},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39910000562667847},{"id":"https://openalex.org/C120330832","wikidata":"https://www.wikidata.org/wiki/Q166656","display_name":"Supply and demand","level":2,"score":0.3880999982357025},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.34459999203681946},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.34389999508857727},{"id":"https://openalex.org/C2779438525","wikidata":"https://www.wikidata.org/wiki/Q5255048","display_name":"Demand response","level":3,"score":0.33399999141693115},{"id":"https://openalex.org/C13736549","wikidata":"https://www.wikidata.org/wiki/Q4489420","display_name":"Industrial engineering","level":1,"score":0.3257000148296356},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.3197999894618988},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3131999969482422},{"id":"https://openalex.org/C48840187","wikidata":"https://www.wikidata.org/wiki/Q689042","display_name":"Service management","level":4,"score":0.3052999973297119},{"id":"https://openalex.org/C32597650","wikidata":"https://www.wikidata.org/wiki/Q5255044","display_name":"Demand patterns","level":3,"score":0.3050999939441681},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.30300000309944153},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2962999939918518},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2800999879837036},{"id":"https://openalex.org/C2779370713","wikidata":"https://www.wikidata.org/wiki/Q357554","display_name":"Load management","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26440000534057617},{"id":"https://openalex.org/C2986470052","wikidata":"https://www.wikidata.org/wiki/Q126793","display_name":"Retail industry","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.257999986410141},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.2549999952316284},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C139749660","wikidata":"https://www.wikidata.org/wiki/Q5657855","display_name":"Sales management","level":2,"score":0.2531999945640564},{"id":"https://openalex.org/C55416958","wikidata":"https://www.wikidata.org/wiki/Q6206757","display_name":"Job shop scheduling","level":3,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2026.3656390","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3656390","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W2510642588","https://openalex.org/W2896827527","https://openalex.org/W2904832339","https://openalex.org/W2914763776","https://openalex.org/W2915043045","https://openalex.org/W2936163520","https://openalex.org/W2944851425","https://openalex.org/W2953352227","https://openalex.org/W2982640393","https://openalex.org/W3014191625","https://openalex.org/W3016366056","https://openalex.org/W3024761859","https://openalex.org/W3029422813","https://openalex.org/W3044162788","https://openalex.org/W3044719873","https://openalex.org/W3045355714","https://openalex.org/W3089035854","https://openalex.org/W3100553765","https://openalex.org/W3157175643","https://openalex.org/W3201482372","https://openalex.org/W4220792972","https://openalex.org/W4225936447","https://openalex.org/W4296017688","https://openalex.org/W4300962982","https://openalex.org/W4306955484","https://openalex.org/W4308067623","https://openalex.org/W4385258623","https://openalex.org/W4388923662","https://openalex.org/W4390967327"],"related_works":[],"abstract_inverted_index":{"Short-term":[0],"demand":[1,25,99,138,180],"forecasting":[2,26,148,172,181],"for":[3,97],"retail":[4,98,179],"products":[5],"is":[6],"a":[7,89],"critical":[8],"task":[9],"in":[10,60,73,129],"supply":[11,105,152],"chain":[12,106,153],"management,":[13],"as":[14,33],"it":[15,124],"enhances":[16],"operational":[17],"efficiency":[18],"and":[19,43,58,120,140,150,174,192],"improves":[20],"customer":[21],"experience.":[22],"However,":[23],"existing":[24],"techniques":[27],"based":[28],"on":[29],"time-series":[30,67],"models":[31,45,52,68],"such":[32],"Recurrent":[34],"Neural":[35],"Network":[36,118],"(RNN),":[37],"Long":[38],"Short-Term":[39],"Memory":[40],"(LSTM)":[41],"networks,":[42],"Transformer":[44,91],"suffer":[46],"from":[47],"several":[48],"limitations.":[49],"First,":[50],"previous":[51],"struggle":[53],"to":[54,102,125,146,161],"effectively":[55],"handle":[56,127],"anomalies":[57],"noise":[59,128],"historical":[61,74,130],"sales":[62,75,131,142],"data.":[63],"Second,":[64],"while":[65,133],"traditional":[66,162],"can":[69],"capture":[70],"global":[71],"patterns":[72],"data,":[76],"they":[77],"often":[78],"overlook":[79],"local":[80],"features.":[81],"To":[82],"address":[83],"these":[84],"challenges,":[85],"this":[86],"work":[87],"proposes":[88],"Masked":[90],"with":[92,187],"Mixed":[93],"Features":[94],"(MTMF)":[95],"model":[96,110,168],"forecasting,":[100],"aiming":[101],"optimize":[103],"IoT-driven":[104],"management.":[107],"The":[108],"MTMF":[109,167],"integrates":[111],"the":[112,166,184],"strengths":[113],"of":[114,190,195],"Transformer,":[115],"Temporal":[116],"Convolutional":[117],"(TCN),":[119],"masked":[121],"training,":[122],"enabling":[123],"better":[126],"data":[132],"simultaneously":[134],"capturing":[135],"both":[136],"long-term":[137],"trends":[139],"short-term":[141],"peaks.":[143],"This":[144],"leads":[145],"improved":[147],"accuracy":[149,173],"enhanced":[151],"scheduling":[154],"efficiency.":[155],"Experimental":[156],"results":[157],"demonstrate":[158],"that,":[159],"compared":[160],"deep":[163],"learning":[164],"methods,":[165],"consistently":[169],"achieves":[170],"higher":[171],"greater":[175],"adaptability":[176],"across":[177],"multiple":[178],"tasks,":[182],"achieving":[183],"best":[185],"performance":[186],"an":[188,193],"MSE":[189],"12.6":[191],"MAE":[194],"1.8.":[196]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2026-01-21T00:00:00"}