A nonlinear time-series prediction methodology based on neural networks and tracking signals
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Production |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132022000100226 |
Resumo: | Abstract Paper aims This paper presents a nonlinear time series prediction methodology using Neural Networks and Tracking Signals method to detect bias and their responsiveness to non-random changes in the time series. Originality This study contributes with an innovative approach of nonlinear time series prediction methodology. Furthermore, the Design of Experiments was applied to simulate datasets and to analyze the results of Average Run Length, identifying in which conditions the methodology is efficient. Research method Datasets were generated to simulate different nonlinear time series by changing the error of the series. The methodology was applied to the datasets and the Design of Experiments was implemented to evaluate the results. Lastly, a case study based on total oil and grease was performed. Main findings The results showed that the proposed prediction methodology is an effective way to detect bias in the process when an error is introduced in the nonlinear time series because the mean and the standard deviation of the error have a significant impact on the Average Run Length. Implications for theory and practice This study contributes to a discussion about time series prediction methodology since this new technique could be widely used in several areas to improve forecast accuracy. |
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A nonlinear time-series prediction methodology based on neural networks and tracking signalsNonlinear time seriesTime series forecastingNeural networksTracking signalsDesign of ExperimentsAbstract Paper aims This paper presents a nonlinear time series prediction methodology using Neural Networks and Tracking Signals method to detect bias and their responsiveness to non-random changes in the time series. Originality This study contributes with an innovative approach of nonlinear time series prediction methodology. Furthermore, the Design of Experiments was applied to simulate datasets and to analyze the results of Average Run Length, identifying in which conditions the methodology is efficient. Research method Datasets were generated to simulate different nonlinear time series by changing the error of the series. The methodology was applied to the datasets and the Design of Experiments was implemented to evaluate the results. Lastly, a case study based on total oil and grease was performed. Main findings The results showed that the proposed prediction methodology is an effective way to detect bias in the process when an error is introduced in the nonlinear time series because the mean and the standard deviation of the error have a significant impact on the Average Run Length. Implications for theory and practice This study contributes to a discussion about time series prediction methodology since this new technique could be widely used in several areas to improve forecast accuracy.Associação Brasileira de Engenharia de Produção2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132022000100226Production v.32 2022reponame:Productioninstname:Associação Brasileira de Engenharia de Produção (ABEPRO)instacron:ABEPRO10.1590/0103-6513.20220064info:eu-repo/semantics/openAccessBianchesi,Natália Maria PugginaMatta,Cláudia Eliane daStreitenberger,Simone CarneiroRomão,Estevão LuizBalestrassi,Pedro PauloCosta,Antônio Fernando Brancoeng2022-09-26T00:00:00Zoai:scielo:S0103-65132022000100226Revistahttps://www.scielo.br/j/prod/https://old.scielo.br/oai/scielo-oai.php||production@editoracubo.com.br1980-54110103-6513opendoar:2022-09-26T00:00Production - Associação Brasileira de Engenharia de Produção (ABEPRO)false |
dc.title.none.fl_str_mv |
A nonlinear time-series prediction methodology based on neural networks and tracking signals |
title |
A nonlinear time-series prediction methodology based on neural networks and tracking signals |
spellingShingle |
A nonlinear time-series prediction methodology based on neural networks and tracking signals Bianchesi,Natália Maria Puggina Nonlinear time series Time series forecasting Neural networks Tracking signals Design of Experiments |
title_short |
A nonlinear time-series prediction methodology based on neural networks and tracking signals |
title_full |
A nonlinear time-series prediction methodology based on neural networks and tracking signals |
title_fullStr |
A nonlinear time-series prediction methodology based on neural networks and tracking signals |
title_full_unstemmed |
A nonlinear time-series prediction methodology based on neural networks and tracking signals |
title_sort |
A nonlinear time-series prediction methodology based on neural networks and tracking signals |
author |
Bianchesi,Natália Maria Puggina |
author_facet |
Bianchesi,Natália Maria Puggina Matta,Cláudia Eliane da Streitenberger,Simone Carneiro Romão,Estevão Luiz Balestrassi,Pedro Paulo Costa,Antônio Fernando Branco |
author_role |
author |
author2 |
Matta,Cláudia Eliane da Streitenberger,Simone Carneiro Romão,Estevão Luiz Balestrassi,Pedro Paulo Costa,Antônio Fernando Branco |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Bianchesi,Natália Maria Puggina Matta,Cláudia Eliane da Streitenberger,Simone Carneiro Romão,Estevão Luiz Balestrassi,Pedro Paulo Costa,Antônio Fernando Branco |
dc.subject.por.fl_str_mv |
Nonlinear time series Time series forecasting Neural networks Tracking signals Design of Experiments |
topic |
Nonlinear time series Time series forecasting Neural networks Tracking signals Design of Experiments |
description |
Abstract Paper aims This paper presents a nonlinear time series prediction methodology using Neural Networks and Tracking Signals method to detect bias and their responsiveness to non-random changes in the time series. Originality This study contributes with an innovative approach of nonlinear time series prediction methodology. Furthermore, the Design of Experiments was applied to simulate datasets and to analyze the results of Average Run Length, identifying in which conditions the methodology is efficient. Research method Datasets were generated to simulate different nonlinear time series by changing the error of the series. The methodology was applied to the datasets and the Design of Experiments was implemented to evaluate the results. Lastly, a case study based on total oil and grease was performed. Main findings The results showed that the proposed prediction methodology is an effective way to detect bias in the process when an error is introduced in the nonlinear time series because the mean and the standard deviation of the error have a significant impact on the Average Run Length. Implications for theory and practice This study contributes to a discussion about time series prediction methodology since this new technique could be widely used in several areas to improve forecast accuracy. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132022000100226 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132022000100226 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0103-6513.20220064 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Engenharia de Produção |
publisher.none.fl_str_mv |
Associação Brasileira de Engenharia de Produção |
dc.source.none.fl_str_mv |
Production v.32 2022 reponame:Production instname:Associação Brasileira de Engenharia de Produção (ABEPRO) instacron:ABEPRO |
instname_str |
Associação Brasileira de Engenharia de Produção (ABEPRO) |
instacron_str |
ABEPRO |
institution |
ABEPRO |
reponame_str |
Production |
collection |
Production |
repository.name.fl_str_mv |
Production - Associação Brasileira de Engenharia de Produção (ABEPRO) |
repository.mail.fl_str_mv |
||production@editoracubo.com.br |
_version_ |
1754213154881011712 |