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Forecasting model selection through out-of-sample rolling horizon weighted errors

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Forecasting model selection through out-of-sample rolling horizon weighted errors

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dc.contributor.author Poler Escoto, Raúl es_ES
dc.contributor.author Mula, Josefa es_ES
dc.date.accessioned 2015-06-03T11:22:13Z
dc.date.available 2015-06-03T11:22:13Z
dc.date.issued 2011-11
dc.identifier.issn 0957-4174
dc.identifier.uri http://hdl.handle.net/10251/51211
dc.description.abstract Demand forecasting is an essential process for any firm whether it is a supplier, manufacturer or retailer. A large number of research works about time series forecast techniques exists in the literature, and there are many time series forecasting tools. In many cases, however, selecting the best time series forecasting model for each time series to be dealt with is still a complex problem. In this paper, a new automatic selection procedure of time series forecasting models is proposed. The selection criterion has been tested using the set of monthly time series of the M3 Competition and two basic forecasting models obtaining interesting results. This selection criterion has been implemented in a forecasting expert system and applied to a real case, a firm that produces steel products for construction, which automatically performs monthly forecasts on tens of thousands of time series. As result, the firm has increased the level of success in its demand forecasts. © 2011 Elsevier Ltd. All rights reserved. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Expert Systems with Applications es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Automatic forecasting es_ES
dc.subject Error measures es_ES
dc.subject Expert system es_ES
dc.subject Forecasting model selection es_ES
dc.subject Time series es_ES
dc.subject Automatic selection es_ES
dc.subject Complex problems es_ES
dc.subject Demand forecast es_ES
dc.subject Demand forecasting es_ES
dc.subject Forecasting models es_ES
dc.subject Rolling horizon es_ES
dc.subject Selection criteria es_ES
dc.subject Steel products es_ES
dc.subject Time series forecasting es_ES
dc.subject Time series forecasts es_ES
dc.subject Expert systems es_ES
dc.subject Forecasting es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.title Forecasting model selection through out-of-sample rolling horizon weighted errors es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.eswa.2011.05.072
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Centro de Investigación de Gestión e Ingeniería de la Producción - Centre d'Investigació de Gestió i Enginyeria de la Producció es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses es_ES
dc.description.bibliographicCitation Poler Escoto, R.; Mula, J. (2011). Forecasting model selection through out-of-sample rolling horizon weighted errors. Expert Systems with Applications. 38(12):14778-14785. doi:10.1016/j.eswa.2011.05.072 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/10.1016/j.eswa.2011.05.072 es_ES
dc.description.upvformatpinicio 14778 es_ES
dc.description.upvformatpfin 14785 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 38 es_ES
dc.description.issue 12 es_ES
dc.relation.senia 200408


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