Estimates by bootstrap interval for time series forecasts obtained by theta model

Detalhes bibliográficos
Autor(a) principal: Steffen, Daniel
Data de Publicação: 2017
Outros Autores: Chaves Neto, Anselmo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Independent Journal of Management & Production
Texto Completo: http://www.ijmp.jor.br/index.php/ijmp/article/view/480
Resumo: In this work, are developed an experimental computer program in Matlab language version 7.1 from the univariate method for time series forecasting called Theta, and implementation of resampling technique known as computer intensive "bootstrap" to estimate the prediction for the point forecast obtained by this method by confidence interval. To solve this problem built up an algorithm that uses Monte Carlo simulation to obtain the interval estimation for forecasts. The Theta model presented in this work was very efficient in M3 Makridakis competition, where tested 3003 series. It is based on the concept of modifying the local curvature of the time series obtained by a coefficient theta (Θ). In it's simplest approach the time series is decomposed into two lines theta representing terms of long term and short term. The prediction is made by combining the forecast obtained by fitting lines obtained with the theta decomposition. The results of Mape's error obtained for the estimates confirm the favorable results to the method of M3 competition being a good alternative for time series forecast.
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spelling Estimates by bootstrap interval for time series forecasts obtained by theta modelForecastingTime SeriesBootstrapTheta ModelIn this work, are developed an experimental computer program in Matlab language version 7.1 from the univariate method for time series forecasting called Theta, and implementation of resampling technique known as computer intensive "bootstrap" to estimate the prediction for the point forecast obtained by this method by confidence interval. To solve this problem built up an algorithm that uses Monte Carlo simulation to obtain the interval estimation for forecasts. The Theta model presented in this work was very efficient in M3 Makridakis competition, where tested 3003 series. It is based on the concept of modifying the local curvature of the time series obtained by a coefficient theta (Θ). In it's simplest approach the time series is decomposed into two lines theta representing terms of long term and short term. The prediction is made by combining the forecast obtained by fitting lines obtained with the theta decomposition. The results of Mape's error obtained for the estimates confirm the favorable results to the method of M3 competition being a good alternative for time series forecast.Independent2017-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdftext/htmlhttp://www.ijmp.jor.br/index.php/ijmp/article/view/48010.14807/ijmp.v8i1.480Independent Journal of Management & Production; Vol. 8 No. 1 (2017): Independent Journal of Management & Production; 144-1582236-269X2236-269Xreponame:Independent Journal of Management & Productioninstname:Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP)instacron:IJM&Penghttp://www.ijmp.jor.br/index.php/ijmp/article/view/480/623http://www.ijmp.jor.br/index.php/ijmp/article/view/480/642Copyright (c) 2017 Daniel Steffen, Anselmo Chaves Netoinfo:eu-repo/semantics/openAccessSteffen, DanielChaves Neto, Anselmo2018-09-04T13:02:47Zoai:www.ijmp.jor.br:article/480Revistahttp://www.ijmp.jor.br/PUBhttp://www.ijmp.jor.br/index.php/ijmp/oaiijmp@ijmp.jor.br||paulo@paulorodrigues.pro.br||2236-269X2236-269Xopendoar:2018-09-04T13:02:47Independent Journal of Management & Production - Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP)false
dc.title.none.fl_str_mv Estimates by bootstrap interval for time series forecasts obtained by theta model
title Estimates by bootstrap interval for time series forecasts obtained by theta model
spellingShingle Estimates by bootstrap interval for time series forecasts obtained by theta model
Steffen, Daniel
Forecasting
Time Series
Bootstrap
Theta Model
title_short Estimates by bootstrap interval for time series forecasts obtained by theta model
title_full Estimates by bootstrap interval for time series forecasts obtained by theta model
title_fullStr Estimates by bootstrap interval for time series forecasts obtained by theta model
title_full_unstemmed Estimates by bootstrap interval for time series forecasts obtained by theta model
title_sort Estimates by bootstrap interval for time series forecasts obtained by theta model
author Steffen, Daniel
author_facet Steffen, Daniel
Chaves Neto, Anselmo
author_role author
author2 Chaves Neto, Anselmo
author2_role author
dc.contributor.author.fl_str_mv Steffen, Daniel
Chaves Neto, Anselmo
dc.subject.por.fl_str_mv Forecasting
Time Series
Bootstrap
Theta Model
topic Forecasting
Time Series
Bootstrap
Theta Model
description In this work, are developed an experimental computer program in Matlab language version 7.1 from the univariate method for time series forecasting called Theta, and implementation of resampling technique known as computer intensive "bootstrap" to estimate the prediction for the point forecast obtained by this method by confidence interval. To solve this problem built up an algorithm that uses Monte Carlo simulation to obtain the interval estimation for forecasts. The Theta model presented in this work was very efficient in M3 Makridakis competition, where tested 3003 series. It is based on the concept of modifying the local curvature of the time series obtained by a coefficient theta (Θ). In it's simplest approach the time series is decomposed into two lines theta representing terms of long term and short term. The prediction is made by combining the forecast obtained by fitting lines obtained with the theta decomposition. The results of Mape's error obtained for the estimates confirm the favorable results to the method of M3 competition being a good alternative for time series forecast.
publishDate 2017
dc.date.none.fl_str_mv 2017-03-01
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 http://www.ijmp.jor.br/index.php/ijmp/article/view/480
10.14807/ijmp.v8i1.480
url http://www.ijmp.jor.br/index.php/ijmp/article/view/480
identifier_str_mv 10.14807/ijmp.v8i1.480
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv http://www.ijmp.jor.br/index.php/ijmp/article/view/480/623
http://www.ijmp.jor.br/index.php/ijmp/article/view/480/642
dc.rights.driver.fl_str_mv Copyright (c) 2017 Daniel Steffen, Anselmo Chaves Neto
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2017 Daniel Steffen, Anselmo Chaves Neto
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
text/html
dc.publisher.none.fl_str_mv Independent
publisher.none.fl_str_mv Independent
dc.source.none.fl_str_mv Independent Journal of Management & Production; Vol. 8 No. 1 (2017): Independent Journal of Management & Production; 144-158
2236-269X
2236-269X
reponame:Independent Journal of Management & Production
instname:Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP)
instacron:IJM&P
instname_str Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP)
instacron_str IJM&P
institution IJM&P
reponame_str Independent Journal of Management & Production
collection Independent Journal of Management & Production
repository.name.fl_str_mv Independent Journal of Management & Production - Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP)
repository.mail.fl_str_mv ijmp@ijmp.jor.br||paulo@paulorodrigues.pro.br||
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