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Pizza sales forecasting using big data analysis

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dc.contributor.authorLee, Daebum-
dc.contributor.authorKim, Juntae-
dc.date.accessioned2024-08-08T08:01:05Z-
dc.date.available2024-08-08T08:01:05Z-
dc.date.issued2015-05-
dc.identifier.issn1343-4500-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/20033-
dc.description.abstractIn today's business environments analysis of big data offers quite remarkable advantage for businesses over their competitors. This study gathers and analyzes big data to propose a pizza sales forecasting model. To that end, the past sales data, and data of events such as holidays, weather, news articles, economic indices, trends, and sports were gathered and used. Sales forecasting methods such as regression analysis and artificial neural network learning model were used to compare the accuracy of forecasting in the presence and absence of collected big data. Experiment results found that the forecasting error rate was improved by over 5% when using big-data.. ©2015 International Information Institute.-
dc.format.extent8-
dc.language영어-
dc.language.isoENG-
dc.publisherInternational Information Institute Ltd.-
dc.titlePizza sales forecasting using big data analysis-
dc.typeArticle-
dc.publisher.location일본-
dc.identifier.scopusid2-s2.0-85000405907-
dc.identifier.bibliographicCitationInformation (Japan), v.18, no.5, pp 1577 - 1584-
dc.citation.titleInformation (Japan)-
dc.citation.volume18-
dc.citation.number5-
dc.citation.startPage1577-
dc.citation.endPage1584-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordAuthorBig data mining-
dc.subject.keywordAuthorData mining algorithms-
dc.subject.keywordAuthorSales forecasting-
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