Machine Learning Algorithms with Intermittent Demand Forecasting: An Application in Retail Apparel with Plenty of Predictors

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dc.authoridSIMSIR, FUAT/0000-0001-7001-5951
dc.authoridUYGUN, OZER/0000-0002-8437-7678
dc.authoridGuven, Ilker/0000-0002-2754-6893
dc.contributor.authorGuven, Ilker
dc.contributor.authorUygun, Ozer
dc.contributor.authorSimsir, Fuat
dc.date.accessioned2024-09-29T16:07:59Z
dc.date.available2024-09-29T16:07:59Z
dc.date.issued2021
dc.departmentKarabük Üniversitesien_US
dc.description.abstractDemand forecasting is a key factor for apparel retail stores to sustain their business, especially where there are variety of products and intermittent demand. In this study, two of the most popular machine learning methods, random forest (RF) and k-nearest neighbour (KNN), have been used to forecast retail apparel's intermittent demand. Numerous variables that may have an effect on the sales, have been taken into account one of which is defined as special day that might trigger intermittency in the demand. During the forecast application, four different datasets were used to provide reliability. 28 different variables were used to increase accuracy of the forecasting and experience of the behaviours of the algorithms. Root mean square error (RMSE) was used to evaluate performance of the methods and as a result of this study, RF showed better performance in all four datasets comparing to KNN.en_US
dc.identifier.doi10.32710/tekstilvekonfeksiyon.809867
dc.identifier.endpage110en_US
dc.identifier.issn1300-3356
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85113869068en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage99en_US
dc.identifier.urihttps://doi.org/10.32710/tekstilvekonfeksiyon.809867
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7298
dc.identifier.volume31en_US
dc.identifier.wosWOS:000691751800004en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherE.U. Printing And Publishing Houseen_US
dc.relation.ispartofTekstil Ve Konfeksiyonen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectIntermittent demanden_US
dc.subjectrandom foresten_US
dc.subjectk-nearest neighbouren_US
dc.subjectretail apparelen_US
dc.subjecttextileen_US
dc.titleMachine Learning Algorithms with Intermittent Demand Forecasting: An Application in Retail Apparel with Plenty of Predictorsen_US
dc.typeArticleen_US

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