A new SVM-based ensemble approach for time series forecasting

Time series analysis has remained as an extremely active research area for decades, receiving a great deal of attention from very different domains like econometrics, statistics, engineering, mathematics, medicine and social sciences. To say nothing about its importance in real-world applications in...

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Autores: Villegas García, Marco Antonio, Pedregal Tercero, Diego José, Trapero, Juan Ramón
Tipo de recurso: artículo
Fecha de publicación:2018
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/20571
Acceso en línea:https://doi.org/10.1016/j.cie.2018.04.042
http://hdl.handle.net/10578/20571
Access Level:acceso abierto
Palabra clave:Demand forecasting
Supply chain
SVM
Time series analysis
Model selection
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spelling A new SVM-based ensemble approach for time series forecastingVillegas García, Marco AntonioPedregal Tercero, Diego JoséTrapero, Juan RamónDemand forecastingSupply chainSVMTime series analysisModel selectionTime series analysis has remained as an extremely active research area for decades, receiving a great deal of attention from very different domains like econometrics, statistics, engineering, mathematics, medicine and social sciences. To say nothing about its importance in real-world applications in a wide variety of industrial and business scenarios. However, as hardware becomes ubiquitous, the amounts of data collected is more and more overwhelming, bringing us all the so-called big data era. It is in this context where automatic time series analysis deserves especial attention as a mean of making sense of such enormous databases. Nevertheless, the automatic identification of the appropriate data modelling techniques stands in the middle as a compulsory stage of any big data implementation. Research on model selection and combination points out the benefits of such techniques in terms of forecast accuracy and reliability. This study proposes a novel ensemble approach for automatic time series forecasting as a part of a big data implementation. Given a set of alternative models, a Support Vector Machine (SVM) is trained at each forecasting origin to select the best model, according to the computed features and the past performance. The feature space embeds information of the time series itself as well as responses and parameters of the alternative models. This approach will help to reduce the risk of misusing modelling techniques when dealing with big datasets, and at the same time will provide a mechanism to assert the appropriateness of the underlying models used to analyse such data. The effects of the proposed approach are explored empirically using a set of representative forecasting methods and a dataset of 229 weekly demand series from a leading household and personal care UK manufacturer. Findings suggest that the proposed approach results in more robust predictions with lower mean forecasting error and biases than base forecasts.Elsevier201920192018info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://doi.org/10.1016/j.cie.2018.04.042http://hdl.handle.net/10578/20571reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésDPI2015-64133-Rinfo:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/205712026-05-27T07:36:41Z
dc.title.none.fl_str_mv A new SVM-based ensemble approach for time series forecasting
title A new SVM-based ensemble approach for time series forecasting
spellingShingle A new SVM-based ensemble approach for time series forecasting
Villegas García, Marco Antonio
Demand forecasting
Supply chain
SVM
Time series analysis
Model selection
title_short A new SVM-based ensemble approach for time series forecasting
title_full A new SVM-based ensemble approach for time series forecasting
title_fullStr A new SVM-based ensemble approach for time series forecasting
title_full_unstemmed A new SVM-based ensemble approach for time series forecasting
title_sort A new SVM-based ensemble approach for time series forecasting
dc.creator.none.fl_str_mv Villegas García, Marco Antonio
Pedregal Tercero, Diego José
Trapero, Juan Ramón
author Villegas García, Marco Antonio
author_facet Villegas García, Marco Antonio
Pedregal Tercero, Diego José
Trapero, Juan Ramón
author_role author
author2 Pedregal Tercero, Diego José
Trapero, Juan Ramón
author2_role author
author
dc.subject.none.fl_str_mv Demand forecasting
Supply chain
SVM
Time series analysis
Model selection
topic Demand forecasting
Supply chain
SVM
Time series analysis
Model selection
description Time series analysis has remained as an extremely active research area for decades, receiving a great deal of attention from very different domains like econometrics, statistics, engineering, mathematics, medicine and social sciences. To say nothing about its importance in real-world applications in a wide variety of industrial and business scenarios. However, as hardware becomes ubiquitous, the amounts of data collected is more and more overwhelming, bringing us all the so-called big data era. It is in this context where automatic time series analysis deserves especial attention as a mean of making sense of such enormous databases. Nevertheless, the automatic identification of the appropriate data modelling techniques stands in the middle as a compulsory stage of any big data implementation. Research on model selection and combination points out the benefits of such techniques in terms of forecast accuracy and reliability. This study proposes a novel ensemble approach for automatic time series forecasting as a part of a big data implementation. Given a set of alternative models, a Support Vector Machine (SVM) is trained at each forecasting origin to select the best model, according to the computed features and the past performance. The feature space embeds information of the time series itself as well as responses and parameters of the alternative models. This approach will help to reduce the risk of misusing modelling techniques when dealing with big datasets, and at the same time will provide a mechanism to assert the appropriateness of the underlying models used to analyse such data. The effects of the proposed approach are explored empirically using a set of representative forecasting methods and a dataset of 229 weekly demand series from a leading household and personal care UK manufacturer. Findings suggest that the proposed approach results in more robust predictions with lower mean forecasting error and biases than base forecasts.
publishDate 2018
dc.date.none.fl_str_mv 2018
2019
2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.cie.2018.04.042
http://hdl.handle.net/10578/20571
url https://doi.org/10.1016/j.cie.2018.04.042
http://hdl.handle.net/10578/20571
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv DPI2015-64133-R
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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