{"id":"https://openalex.org/W4402138590","doi":"https://doi.org/10.1007/s00607-024-01320-y","title":"A demand forecasting system of product categories defined by their time series using a hybrid approach of ensemble learning with feature engineering","display_name":"A demand forecasting system of product categories defined by their time series using a hybrid approach of ensemble learning with feature engineering","publication_year":2024,"publication_date":"2024-09-02","ids":{"openalex":"https://openalex.org/W4402138590","doi":"https://doi.org/10.1007/s00607-024-01320-y"},"language":"en","primary_location":{"id":"doi:10.1007/s00607-024-01320-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00607-024-01320-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00607-024-01320-y.pdf","source":{"id":"https://openalex.org/S35593046","display_name":"Computing","issn_l":"0010-485X","issn":["0010-485X","1436-5057"],"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":"Computing","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/s00607-024-01320-y.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021290297","display_name":"Santiago Mej\u00eda","orcid":null},"institutions":[{"id":"https://openalex.org/I862322245","display_name":"Universidad EAFIT","ror":"https://ror.org/03y3y9v44","country_code":"CO","type":"education","lineage":["https://openalex.org/I862322245"]}],"countries":["CO"],"is_corresponding":false,"raw_author_name":"Santiago Mej\u00eda","raw_affiliation_strings":["GIDITIC, Universidad EAFIT, Medell\u00edn, Colombia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"GIDITIC, Universidad EAFIT, Medell\u00edn, Colombia","institution_ids":["https://openalex.org/I862322245"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042045106","display_name":"Jos\u00e9 Aguilar","orcid":"https://orcid.org/0000-0003-4194-6882"},"institutions":[{"id":"https://openalex.org/I203303854","display_name":"University of the Andes","ror":"https://ror.org/02h1b1x27","country_code":"VE","type":"education","lineage":["https://openalex.org/I203303854"]},{"id":"https://openalex.org/I2802499160","display_name":"IMDEA Networks","ror":"https://ror.org/04mm9fg30","country_code":"ES","type":"facility","lineage":["https://openalex.org/I105140100","https://openalex.org/I2802499160"]},{"id":"https://openalex.org/I862322245","display_name":"Universidad EAFIT","ror":"https://ror.org/03y3y9v44","country_code":"CO","type":"education","lineage":["https://openalex.org/I862322245"]}],"countries":["CO","ES","VE"],"is_corresponding":true,"raw_author_name":"Jose Aguilar","raw_affiliation_strings":["CEMISID, Universidad de Los Andes, M\u00e9rida, Venezuela","GIDITIC, Universidad EAFIT, Medell\u00edn, Colombia","IMDEA Networks Institute, Legan\u00e9s, Madrid, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CEMISID, Universidad de Los Andes, M\u00e9rida, Venezuela","institution_ids":["https://openalex.org/I203303854"]},{"raw_affiliation_string":"GIDITIC, Universidad EAFIT, Medell\u00edn, Colombia","institution_ids":["https://openalex.org/I862322245"]},{"raw_affiliation_string":"IMDEA Networks Institute, Legan\u00e9s, Madrid, Spain","institution_ids":["https://openalex.org/I2802499160"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5042045106"],"corresponding_institution_ids":["https://openalex.org/I203303854","https://openalex.org/I2802499160","https://openalex.org/I862322245"],"apc_list":{"value":2290,"currency":"EUR","value_usd":2890},"apc_paid":{"value":2290,"currency":"EUR","value_usd":2890},"fwci":3.651,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.93981138,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"106","issue":"12","first_page":"3945","last_page":"3965"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9850000143051147,"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":0.9850000143051147,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9613000154495239,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.7220445275306702},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6415572166442871},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5754153728485107},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.5727003812789917},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.5584036707878113},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5109497904777527},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.45766642689704895},{"id":"https://openalex.org/keywords/demand-forecasting","display_name":"Demand forecasting","score":0.4418957233428955},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4314824342727661},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3956409990787506},{"id":"https://openalex.org/keywords/industrial-engineering","display_name":"Industrial engineering","score":0.38538891077041626},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3425416946411133},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32617712020874023},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24889150261878967},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.20058488845825195},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1510685384273529},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.1027064323425293},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.07186862826347351}],"concepts":[{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7220445275306702},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6415572166442871},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5754153728485107},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.5727003812789917},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.5584036707878113},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5109497904777527},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.45766642689704895},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.4418957233428955},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4314824342727661},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3956409990787506},{"id":"https://openalex.org/C13736549","wikidata":"https://www.wikidata.org/wiki/Q4489420","display_name":"Industrial engineering","level":1,"score":0.38538891077041626},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3425416946411133},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32617712020874023},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24889150261878967},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.20058488845825195},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1510685384273529},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.1027064323425293},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.07186862826347351},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s00607-024-01320-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00607-024-01320-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00607-024-01320-y.pdf","source":{"id":"https://openalex.org/S35593046","display_name":"Computing","issn_l":"0010-485X","issn":["0010-485X","1436-5057"],"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":"Computing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s00607-024-01320-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00607-024-01320-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00607-024-01320-y.pdf","source":{"id":"https://openalex.org/S35593046","display_name":"Computing","issn_l":"0010-485X","issn":["0010-485X","1436-5057"],"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":"Computing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4402138590.pdf","grobid_xml":"https://content.openalex.org/works/W4402138590.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1689711448","https://openalex.org/W2340426395","https://openalex.org/W2802314367","https://openalex.org/W2900296094","https://openalex.org/W2909973217","https://openalex.org/W2945257941","https://openalex.org/W2946015029","https://openalex.org/W2954016270","https://openalex.org/W2963311488","https://openalex.org/W2965971168","https://openalex.org/W2970633746","https://openalex.org/W2971724044","https://openalex.org/W2979950223","https://openalex.org/W2999406809","https://openalex.org/W2999934768","https://openalex.org/W3008696509","https://openalex.org/W3016649969","https://openalex.org/W3029422813","https://openalex.org/W3081020639","https://openalex.org/W3087757614","https://openalex.org/W3119490477","https://openalex.org/W3127007725","https://openalex.org/W3128920785","https://openalex.org/W3146457390","https://openalex.org/W3165575966","https://openalex.org/W3176063409","https://openalex.org/W3188107912","https://openalex.org/W4205550876","https://openalex.org/W4283259175","https://openalex.org/W4306672672","https://openalex.org/W4380319940","https://openalex.org/W4381735942","https://openalex.org/W4381857211","https://openalex.org/W4382540013","https://openalex.org/W4385492466","https://openalex.org/W4399572290"],"related_works":["https://openalex.org/W4387478977","https://openalex.org/W2598381895","https://openalex.org/W1553072606","https://openalex.org/W3034338377","https://openalex.org/W2373792516","https://openalex.org/W2364654346","https://openalex.org/W2538183362","https://openalex.org/W4287991004","https://openalex.org/W4389131438","https://openalex.org/W4205436246"],"abstract_inverted_index":{"Abstract":[0],"Retail":[1],"companies":[2],"face":[3],"major":[4],"problems":[5],"in":[6,41],"the":[7,16,33,44,68,90,93,101,145,154,161,166,170,176],"estimation":[8,168],"of":[9,19,43,67,72,95,105,122,148],"their":[10],"product\u2019s":[11],"future":[12],"demand":[13,34,69,103,155,167],"due":[14],"to":[15,31,53,128,151],"high":[17],"diversity":[18],"sales":[20],"behavior":[21],"that":[22,58,87,160],"each":[23,73,106,135,149],"good":[24],"presents.":[25],"Different":[26],"forecasting":[27,77,124,163,172],"models":[28,141,147],"are":[29,61,126,142],"implemented":[30,143],"meet":[32],"requirements":[35],"for":[36,64,134],"efficient":[37],"inventory":[38],"management.":[39],"However,":[40],"most":[42],"proposed":[45,76,162],"works,":[46],"a":[47,84,111,116,120],"single":[48,171],"model":[49],"approach":[50,99],"is":[51],"applied":[52],"forecast":[54],"all":[55],"products,":[56],"ignoring":[57],"some":[59],"methods":[60,125],"better":[62],"adapted":[63],"certain":[65],"features":[66,104],"time":[70],"series":[71],"product.":[74],"The":[75,157],"system":[78,164],"addresses":[79],"this":[80],"problem,":[81],"by":[82],"implementing":[83],"two-phase":[85],"methodology":[86],"initially":[88],"clusters":[89],"products":[91],"with":[92,131],"application":[94],"an":[96],"unsupervised":[97],"learning":[98,140],"using":[100,144],"extracted":[102],"good,":[107],"and":[108,181],"then,":[109],"implements":[110],"second":[112],"phase":[113],"where,":[114],"after":[115],"feature":[117],"engineering":[118],"process,":[119],"set":[121],"different":[123],"evaluated":[127],"identify":[129],"those":[130],"best":[132],"performs":[133],"cluster.":[136],"Finally,":[137],"ensemble":[138],"machine":[139],"top-performing":[146],"cluster":[150],"carry":[152],"out":[153],"estimation.":[156],"results":[158],"indicate":[159],"improves":[165],"over":[169],"approaches":[173],"when":[174],"evaluating":[175],"R":[177],"2":[178],",":[179],"MSE,":[180],"MASE":[182],"quality":[183],"measures.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-13T06:13:01.061226","created_date":"2025-10-10T00:00:00"}