BOX & JENKINS METHODOLOGY: AN APLICATION IN RAW MILK DATA FROM THE STATE OF MINAS GERAIS
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Revista do Instituto de Laticínios Cândido Tostes |
Texto Completo: | https://www.revistadoilct.com.br/rilct/article/view/286 |
Resumo: | The Box Jenkins methodology was used to obtain a statistical model for estimate the production in liters of milk of the 6 first months of 2013 in Minas Gerais state, adjusting SARIMA (p, d, q) x (P, D, Q)s models, where d and D are the number of differences to remove the trend and seasonality of time series, p and q are the order of the autoregressive and moving average operators, P and Q are the order of theautoregressive and moving average seasonal operators and s is the seasonal periodicity.The Akaike Criterion Information (AIC) procedure was used to select the 6 mostparsimonious models and to find the best one the error indicators Mean Squared Error(EQM) and Mean Absolute Percent Error (MAPE) were analyzed, in addition to theassumptions of residues white noise. The Seasonal Autoregressive Integrated MovingAverage SARIMA (3,1,2) x (0,1,2)12 was upper, view of the principle of parsimonyand with more precise estimates. The forecast was more adjusted to the real valuesof milk production in Minas Gerais state and the model had smaller error indicators.The residues estimated were by this model white noise. |
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BOX & JENKINS METHODOLOGY: AN APLICATION IN RAW MILK DATA FROM THE STATE OF MINAS GERAISMETODOLOGIA BOX & JENKINS: UMA APLICAÇÃO EM DADOS DE PRODUÇÃO DE LEITE CRU DO ESTADO DE MINAS GERAISforecasting; modeling; trend; seasonalityLaticínios; estatísticaprevisão; modelagem; tendência; sazonalidadeThe Box Jenkins methodology was used to obtain a statistical model for estimate the production in liters of milk of the 6 first months of 2013 in Minas Gerais state, adjusting SARIMA (p, d, q) x (P, D, Q)s models, where d and D are the number of differences to remove the trend and seasonality of time series, p and q are the order of the autoregressive and moving average operators, P and Q are the order of theautoregressive and moving average seasonal operators and s is the seasonal periodicity.The Akaike Criterion Information (AIC) procedure was used to select the 6 mostparsimonious models and to find the best one the error indicators Mean Squared Error(EQM) and Mean Absolute Percent Error (MAPE) were analyzed, in addition to theassumptions of residues white noise. The Seasonal Autoregressive Integrated MovingAverage SARIMA (3,1,2) x (0,1,2)12 was upper, view of the principle of parsimonyand with more precise estimates. The forecast was more adjusted to the real valuesof milk production in Minas Gerais state and the model had smaller error indicators.The residues estimated were by this model white noise.Utilizou-se a metodologia Box Jenkins para obter um modelo estatístico que estimasse a produção de litros de leite dos seis primeiros meses de 2013 no estado de Minas Gerais, ajustando modelos SARIMA (p, d, q) x (P, D, Q)s, no qual d e D são o número de diferenças necessárias para remover a tendência e sazonalidade da série, p e q a ordem dos operadores autoregressivos e de médias móveis, P e Q a ordem dos operadores autoregressivos e de móveis sazonais e s a periodicidade sazonal. Por meio do Critério de Informação de Akaike (AIC) selecionou-se os seis modelos mais parcimoniosos e para encontrar o melhor foram analisados os indicadores Erro Quadrático Médio (EQM) e Erro Percentual Médio Absoluto (MAPE), além das pressuposições de resíduos ruído branco. O modelo Autoregressivo Integrado e de Médias Móveis Sazonal SARIMA (3,1,2) x (0,1,2)12 foi superior, pois atendeu ao princípio da parcimônia, obteve estimativas de produção de leite mais ajustadas e consequentemente menores valores para os indicadores de erro EQM e MAPE. Os resíduos estimados por este modelo foram ruído branco.ILCTBarbosa, Eduardo CampanaSáfadi, ThelmaHenrique Osório Silva, CarlosCésar Manuli, Rômulo2014-05-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistadoilct.com.br/rilct/article/view/28610.14295/2238-6416.v69i2.286Journal of Candido Tostes Dairy Institute; v. 69, n. 2 (2014); 129-139Revista do Instituto de Laticínios Cândido Tostes; v. 69, n. 2 (2014); 129-1392238-64160100-3674reponame:Revista do Instituto de Laticínios Cândido Tostesinstname:Empresa de Pesquisa Agropecuária de Minas Gerais (EPAMIG)instacron:EPAMIGporhttps://www.revistadoilct.com.br/rilct/article/view/286/299Direitos autorais 2014 Revista do Instituto de Laticínios Cândido Tostesinfo:eu-repo/semantics/openAccess2014-05-20T14:14:56Zoai:oai.rilct.emnuvens.com.br:article/286Revistahttp://www.revistadoilct.com.br/ONGhttps://www.revistadoilct.com.br/rilct/oai||revistadoilct@epamig.br|| revistadoilct@oi.com.br2238-64160100-3674opendoar:2014-05-20T14:14:56Revista do Instituto de Laticínios Cândido Tostes - Empresa de Pesquisa Agropecuária de Minas Gerais (EPAMIG)false |
dc.title.none.fl_str_mv |
BOX & JENKINS METHODOLOGY: AN APLICATION IN RAW MILK DATA FROM THE STATE OF MINAS GERAIS METODOLOGIA BOX & JENKINS: UMA APLICAÇÃO EM DADOS DE PRODUÇÃO DE LEITE CRU DO ESTADO DE MINAS GERAIS |
title |
BOX & JENKINS METHODOLOGY: AN APLICATION IN RAW MILK DATA FROM THE STATE OF MINAS GERAIS |
spellingShingle |
BOX & JENKINS METHODOLOGY: AN APLICATION IN RAW MILK DATA FROM THE STATE OF MINAS GERAIS Barbosa, Eduardo Campana forecasting; modeling; trend; seasonality Laticínios; estatística previsão; modelagem; tendência; sazonalidade |
title_short |
BOX & JENKINS METHODOLOGY: AN APLICATION IN RAW MILK DATA FROM THE STATE OF MINAS GERAIS |
title_full |
BOX & JENKINS METHODOLOGY: AN APLICATION IN RAW MILK DATA FROM THE STATE OF MINAS GERAIS |
title_fullStr |
BOX & JENKINS METHODOLOGY: AN APLICATION IN RAW MILK DATA FROM THE STATE OF MINAS GERAIS |
title_full_unstemmed |
BOX & JENKINS METHODOLOGY: AN APLICATION IN RAW MILK DATA FROM THE STATE OF MINAS GERAIS |
title_sort |
BOX & JENKINS METHODOLOGY: AN APLICATION IN RAW MILK DATA FROM THE STATE OF MINAS GERAIS |
author |
Barbosa, Eduardo Campana |
author_facet |
Barbosa, Eduardo Campana Sáfadi, Thelma Henrique Osório Silva, Carlos César Manuli, Rômulo |
author_role |
author |
author2 |
Sáfadi, Thelma Henrique Osório Silva, Carlos César Manuli, Rômulo |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
|
dc.contributor.author.fl_str_mv |
Barbosa, Eduardo Campana Sáfadi, Thelma Henrique Osório Silva, Carlos César Manuli, Rômulo |
dc.subject.none.fl_str_mv |
|
dc.subject.por.fl_str_mv |
forecasting; modeling; trend; seasonality Laticínios; estatística previsão; modelagem; tendência; sazonalidade |
topic |
forecasting; modeling; trend; seasonality Laticínios; estatística previsão; modelagem; tendência; sazonalidade |
description |
The Box Jenkins methodology was used to obtain a statistical model for estimate the production in liters of milk of the 6 first months of 2013 in Minas Gerais state, adjusting SARIMA (p, d, q) x (P, D, Q)s models, where d and D are the number of differences to remove the trend and seasonality of time series, p and q are the order of the autoregressive and moving average operators, P and Q are the order of theautoregressive and moving average seasonal operators and s is the seasonal periodicity.The Akaike Criterion Information (AIC) procedure was used to select the 6 mostparsimonious models and to find the best one the error indicators Mean Squared Error(EQM) and Mean Absolute Percent Error (MAPE) were analyzed, in addition to theassumptions of residues white noise. The Seasonal Autoregressive Integrated MovingAverage SARIMA (3,1,2) x (0,1,2)12 was upper, view of the principle of parsimonyand with more precise estimates. The forecast was more adjusted to the real valuesof milk production in Minas Gerais state and the model had smaller error indicators.The residues estimated were by this model white noise. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-05-05 |
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://www.revistadoilct.com.br/rilct/article/view/286 10.14295/2238-6416.v69i2.286 |
url |
https://www.revistadoilct.com.br/rilct/article/view/286 |
identifier_str_mv |
10.14295/2238-6416.v69i2.286 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://www.revistadoilct.com.br/rilct/article/view/286/299 |
dc.rights.driver.fl_str_mv |
Direitos autorais 2014 Revista do Instituto de Laticínios Cândido Tostes info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Direitos autorais 2014 Revista do Instituto de Laticínios Cândido Tostes |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
ILCT |
publisher.none.fl_str_mv |
ILCT |
dc.source.none.fl_str_mv |
Journal of Candido Tostes Dairy Institute; v. 69, n. 2 (2014); 129-139 Revista do Instituto de Laticínios Cândido Tostes; v. 69, n. 2 (2014); 129-139 2238-6416 0100-3674 reponame:Revista do Instituto de Laticínios Cândido Tostes instname:Empresa de Pesquisa Agropecuária de Minas Gerais (EPAMIG) instacron:EPAMIG |
instname_str |
Empresa de Pesquisa Agropecuária de Minas Gerais (EPAMIG) |
instacron_str |
EPAMIG |
institution |
EPAMIG |
reponame_str |
Revista do Instituto de Laticínios Cândido Tostes |
collection |
Revista do Instituto de Laticínios Cândido Tostes |
repository.name.fl_str_mv |
Revista do Instituto de Laticínios Cândido Tostes - Empresa de Pesquisa Agropecuária de Minas Gerais (EPAMIG) |
repository.mail.fl_str_mv |
||revistadoilct@epamig.br|| revistadoilct@oi.com.br |
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1809738130851364864 |