Predicting the GFCF of the Brazilian construction industry: a comparison between Holt Winters' and SARIMA models
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista Gestão Industrial |
Texto Completo: | https://periodicos.utfpr.edu.br/revistagi/article/view/8590 |
Resumo: | The present study focuses on creating a forecasting model in order to predict the behavior of the economic indicator known as Gross Fixed Capital Formation (GFCF) of the Brazilian construction industry, which reflects the amount of investment in the construction industry sector. The data set consists of monthly observations from January 1996 to December 2016, the year of 2016 is used as validation for the forecast model. As strong seasonality was identified in the time series, Seasonal Autoregressive Integrated Moving Average (SARIMA) and Holt Winters' models are applied and compared. After the evaluation of the selected models, the ARIMA (2,1,2) × (0,1,1)12 is identified as the best forecast model with reasonable deviations. However, the damped multiplicative Holt Winters' model also produces good results, despite its inability in eliminating the autocorrelation in the residuals. Therefore, both models can predict the GFCF with good accuracy, which can be useful for decision-making by investors and business managers. |
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Predicting the GFCF of the Brazilian construction industry: a comparison between Holt Winters' and SARIMA modelsEngenharia de Produção; Séries TemporaisSARIMA; Box-Jenkins; Forecasting; Holt Winters; GFCF.The present study focuses on creating a forecasting model in order to predict the behavior of the economic indicator known as Gross Fixed Capital Formation (GFCF) of the Brazilian construction industry, which reflects the amount of investment in the construction industry sector. The data set consists of monthly observations from January 1996 to December 2016, the year of 2016 is used as validation for the forecast model. As strong seasonality was identified in the time series, Seasonal Autoregressive Integrated Moving Average (SARIMA) and Holt Winters' models are applied and compared. After the evaluation of the selected models, the ARIMA (2,1,2) × (0,1,1)12 is identified as the best forecast model with reasonable deviations. However, the damped multiplicative Holt Winters' model also produces good results, despite its inability in eliminating the autocorrelation in the residuals. Therefore, both models can predict the GFCF with good accuracy, which can be useful for decision-making by investors and business managers.Universidade Tecnológica Federal do Paraná (UTFPR)CAPESThomaz, Paulo SigaDias de Mattos, Viviane Leite2019-03-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.utfpr.edu.br/revistagi/article/view/859010.3895/gi.v15n1.8590Revista Gestão Industrial; v. 15, n. 1 (2019)1808-044810.3895/gi.v15n1reponame:Revista Gestão Industrialinstname:Universidade Tecnológica Federal do Paraná (UTFPR)instacron:UTFPRenghttps://periodicos.utfpr.edu.br/revistagi/article/view/8590/5964Direitos autorais 2019 CC-BYhttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccess2019-07-11T11:29:30Zoai:periodicos.utfpr:article/8590Revistahttps://periodicos.utfpr.edu.br/revistagiPUBhttps://periodicos.utfpr.edu.br/revistagi/oai||revistagi@utfpr.edu.br1808-04481808-0448opendoar:2019-07-11T11:29:30Revista Gestão Industrial - Universidade Tecnológica Federal do Paraná (UTFPR)false |
dc.title.none.fl_str_mv |
Predicting the GFCF of the Brazilian construction industry: a comparison between Holt Winters' and SARIMA models |
title |
Predicting the GFCF of the Brazilian construction industry: a comparison between Holt Winters' and SARIMA models |
spellingShingle |
Predicting the GFCF of the Brazilian construction industry: a comparison between Holt Winters' and SARIMA models Thomaz, Paulo Siga Engenharia de Produção; Séries Temporais SARIMA; Box-Jenkins; Forecasting; Holt Winters; GFCF. |
title_short |
Predicting the GFCF of the Brazilian construction industry: a comparison between Holt Winters' and SARIMA models |
title_full |
Predicting the GFCF of the Brazilian construction industry: a comparison between Holt Winters' and SARIMA models |
title_fullStr |
Predicting the GFCF of the Brazilian construction industry: a comparison between Holt Winters' and SARIMA models |
title_full_unstemmed |
Predicting the GFCF of the Brazilian construction industry: a comparison between Holt Winters' and SARIMA models |
title_sort |
Predicting the GFCF of the Brazilian construction industry: a comparison between Holt Winters' and SARIMA models |
author |
Thomaz, Paulo Siga |
author_facet |
Thomaz, Paulo Siga Dias de Mattos, Viviane Leite |
author_role |
author |
author2 |
Dias de Mattos, Viviane Leite |
author2_role |
author |
dc.contributor.none.fl_str_mv |
CAPES |
dc.contributor.author.fl_str_mv |
Thomaz, Paulo Siga Dias de Mattos, Viviane Leite |
dc.subject.por.fl_str_mv |
Engenharia de Produção; Séries Temporais SARIMA; Box-Jenkins; Forecasting; Holt Winters; GFCF. |
topic |
Engenharia de Produção; Séries Temporais SARIMA; Box-Jenkins; Forecasting; Holt Winters; GFCF. |
description |
The present study focuses on creating a forecasting model in order to predict the behavior of the economic indicator known as Gross Fixed Capital Formation (GFCF) of the Brazilian construction industry, which reflects the amount of investment in the construction industry sector. The data set consists of monthly observations from January 1996 to December 2016, the year of 2016 is used as validation for the forecast model. As strong seasonality was identified in the time series, Seasonal Autoregressive Integrated Moving Average (SARIMA) and Holt Winters' models are applied and compared. After the evaluation of the selected models, the ARIMA (2,1,2) × (0,1,1)12 is identified as the best forecast model with reasonable deviations. However, the damped multiplicative Holt Winters' model also produces good results, despite its inability in eliminating the autocorrelation in the residuals. Therefore, both models can predict the GFCF with good accuracy, which can be useful for decision-making by investors and business managers. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-03-08 |
dc.type.none.fl_str_mv |
|
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://periodicos.utfpr.edu.br/revistagi/article/view/8590 10.3895/gi.v15n1.8590 |
url |
https://periodicos.utfpr.edu.br/revistagi/article/view/8590 |
identifier_str_mv |
10.3895/gi.v15n1.8590 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://periodicos.utfpr.edu.br/revistagi/article/view/8590/5964 |
dc.rights.driver.fl_str_mv |
Direitos autorais 2019 CC-BY http://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Direitos autorais 2019 CC-BY http://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Tecnológica Federal do Paraná (UTFPR) |
publisher.none.fl_str_mv |
Universidade Tecnológica Federal do Paraná (UTFPR) |
dc.source.none.fl_str_mv |
Revista Gestão Industrial; v. 15, n. 1 (2019) 1808-0448 10.3895/gi.v15n1 reponame:Revista Gestão Industrial instname:Universidade Tecnológica Federal do Paraná (UTFPR) instacron:UTFPR |
instname_str |
Universidade Tecnológica Federal do Paraná (UTFPR) |
instacron_str |
UTFPR |
institution |
UTFPR |
reponame_str |
Revista Gestão Industrial |
collection |
Revista Gestão Industrial |
repository.name.fl_str_mv |
Revista Gestão Industrial - Universidade Tecnológica Federal do Paraná (UTFPR) |
repository.mail.fl_str_mv |
||revistagi@utfpr.edu.br |
_version_ |
1800237642702389248 |