Estimates by bootstrap interval for time series forecasts obtained by theta model
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | |
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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Independent Journal of Management & Production |
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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|| |
_version_ |
1797220490981408768 |