Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/4616
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dc.contributor.authorSilva,AMen
dc.contributor.authorRita Paula Ribeiroen
dc.contributor.authorJoão Gamaen
dc.date.accessioned2017-12-21T12:03:40Z-
dc.date.available2017-12-21T12:03:40Z-
dc.date.issued2015en
dc.identifier.urihttp://repositorio.inesctec.pt/handle/123456789/4616-
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-23485-4_50en
dc.description.abstractPlanning strategies play an important role in companies' management. In the decision-making process, one of the main important goals is sales forecasting. They are important for stocks planing, shop space maintenance, promotions, etc. Sales forecasting use historical data to make reliable projections for the future. In the retail sector, data has a hierarchical structure. Products are organized in hierarchical groups that reflect the business structure. In this work we present a case study, using real data, from a Portuguese leader retail company. We experimentally evaluate standard approaches for sales forecasting and compare against models that explore the hierarchical structure of the products. Moreover, we evaluate different methods to combine predictions for the different hierarchical levels. The results show that exploiting the hierarchical structure present in the data systematically reduces the error of the forecasts.en
dc.languageengen
dc.relation5120en
dc.relation4983en
dc.rightsinfo:eu-repo/semantics/embargoedAccessen
dc.titleAn Experimental Study on Predictive Models Using Hierarchical Time Seriesen
dc.typeconferenceObjecten
dc.typePublicationen
Appears in Collections:LIAAD - Articles in International Conferences

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