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

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 ti...

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Detalles Bibliográficos
Autores: Poler, R.|||0000-0003-4475-6371, Mula, Josefa|||0000-0002-8447-3387
Tipo de recurso: artículo
Fecha de publicación:2011
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/51211
Acceso en línea:https://riunet.upv.es/handle/10251/51211
Access Level:acceso abierto
Palabra clave:Automatic forecasting
Error measures
Expert system
Forecasting model selection
Time series
Automatic selection
Complex problems
Demand forecast
Demand forecasting
Forecasting models
Rolling horizon
Selection criteria
Steel products
Time series forecasting
Time series forecasts
Expert systems
Forecasting
ORGANIZACION DE EMPRESAS
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oai_identifier_str oai:riunet.upv.es:10251/51211
network_acronym_str ES
network_name_str España
repository_id_str
spelling Forecasting model selection through out-of-sample rolling horizon weighted errorsPoler, R.|||0000-0003-4475-6371Mula, Josefa|||0000-0002-8447-3387Automatic forecastingError measuresExpert systemForecasting model selectionTime seriesAutomatic selectionComplex problemsDemand forecastDemand forecastingForecasting modelsRolling horizonSelection criteriaSteel productsTime series forecastingTime series forecastsExpert systemsForecastingORGANIZACION DE EMPRESASDemand 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.ElsevierDepartamento de Organización de EmpresasCentro de Investigación en Gestión e Ingeniería de ProducciónEscuela Politécnica Superior de AlcoyRepositorio Institucional de la Universitat Politècnica de València Riunet20112011-11-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/51211reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reserva de todos los derechoshttp://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/512112026-06-13T07:49:27Z
dc.title.none.fl_str_mv Forecasting model selection through out-of-sample rolling horizon weighted errors
title Forecasting model selection through out-of-sample rolling horizon weighted errors
spellingShingle Forecasting model selection through out-of-sample rolling horizon weighted errors
Poler, R.|||0000-0003-4475-6371
Automatic forecasting
Error measures
Expert system
Forecasting model selection
Time series
Automatic selection
Complex problems
Demand forecast
Demand forecasting
Forecasting models
Rolling horizon
Selection criteria
Steel products
Time series forecasting
Time series forecasts
Expert systems
Forecasting
ORGANIZACION DE EMPRESAS
title_short Forecasting model selection through out-of-sample rolling horizon weighted errors
title_full Forecasting model selection through out-of-sample rolling horizon weighted errors
title_fullStr Forecasting model selection through out-of-sample rolling horizon weighted errors
title_full_unstemmed Forecasting model selection through out-of-sample rolling horizon weighted errors
title_sort Forecasting model selection through out-of-sample rolling horizon weighted errors
dc.creator.none.fl_str_mv Poler, R.|||0000-0003-4475-6371
Mula, Josefa|||0000-0002-8447-3387
author Poler, R.|||0000-0003-4475-6371
author_facet Poler, R.|||0000-0003-4475-6371
Mula, Josefa|||0000-0002-8447-3387
author_role author
author2 Mula, Josefa|||0000-0002-8447-3387
author2_role author
dc.contributor.none.fl_str_mv Departamento de Organización de Empresas
Centro de Investigación en Gestión e Ingeniería de Producción
Escuela Politécnica Superior de Alcoy
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Automatic forecasting
Error measures
Expert system
Forecasting model selection
Time series
Automatic selection
Complex problems
Demand forecast
Demand forecasting
Forecasting models
Rolling horizon
Selection criteria
Steel products
Time series forecasting
Time series forecasts
Expert systems
Forecasting
ORGANIZACION DE EMPRESAS
topic Automatic forecasting
Error measures
Expert system
Forecasting model selection
Time series
Automatic selection
Complex problems
Demand forecast
Demand forecasting
Forecasting models
Rolling horizon
Selection criteria
Steel products
Time series forecasting
Time series forecasts
Expert systems
Forecasting
ORGANIZACION DE EMPRESAS
description 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.
publishDate 2011
dc.date.none.fl_str_mv 2011
2011-11-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/51211
url https://riunet.upv.es/handle/10251/51211
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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