Forecasting of printing and writing paper consumption in Iran using artificial neural network and classical methods

The objective of the research is to forecast the trend of the printing and writing paper consumption in Iran for a five-year period using both modern and classical methods. In order to do the forecasting, predictability of time series was primarily studied using Durbin-Watson and Runs tests. Then, a...

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Autores principales:	Amir Tavakkoli, Amir Hooman Hemmasi, Mohammad Talaeipour, Behzad Bazyar, Ajang Tajdini
Formato:	article
Lenguaje:	FA
Publicado:	Regional Information Center for Science and Technology (RICeST) 2015
Materias:	Printing and writing paper consumption forecasting multivariate econometric exponential smoothing ARIMA multilayer perceptrons neural network Forestry SD1-669.5
Acceso en línea:	https://doaj.org/article/ba5ae395e05c4538a30ed7206f331d12
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spelling	oai:doaj.org-article:ba5ae395e05c4538a30ed7206f331d122021-12-02T05:06:01ZForecasting of printing and writing paper consumption in Iran using artificial neural network and classical methods1735-09132383-112X10.22092/ijwpr.2015.101476https://doaj.org/article/ba5ae395e05c4538a30ed7206f331d122015-12-01T00:00:00Zhttp://ijwpr.areeo.ac.ir/article_101476_16b232317ac9bea70d7aaa5b526f327f.pdfhttps://doaj.org/toc/1735-0913https://doaj.org/toc/2383-112XThe objective of the research is to forecast the trend of the printing and writing paper consumption in Iran for a five-year period using both modern and classical methods. In order to do the forecasting, predictability of time series was primarily studied using Durbin-Watson and Runs tests. Then, artificial neural network model (multilayer perceptrons (MLP)) and univariate and multivariate classical forecasting models such as univariate single exponential smoothing (SES), double exponential smoothing (DES), holt-winters exponential smoothing (HWES) and Box- Jenkins (ARIMA) models, and multivariate econometric model all together were compared in terms of the standard statistical measures. Finally, the consumption of printing and writing paper in Iran was forecasted up to the year 2017 using the most appropriate model. The results of both the parametric test of Durbin-Watson and non-parametric test of Runs show that, the printing and writing consumption series is non-random and predictable. The results of comparing different forecast methods showed that the artificial neural network model has higher forecasting accuracy than the classical models and it is more appropriate for the five-year forecast period. Also, the results of forecasting by using neural network model (MLP), revealed that the printing and writing paper consumption in Iran is forecasted to increase by 5.3%, from around 375 thousand tons in 2012 to 420 thousand tons in 2013, but it falls over the five-year forecast period, from 5.3% in 2013 to 0.07% in 2017.Amir TavakkoliAmir Hooman HemmasiMohammad TalaeipourBehzad BazyarAjang TajdiniRegional Information Center for Science and Technology (RICeST)articlePrinting and writing paper consumptionforecastingmultivariate econometricexponential smoothingARIMAmultilayer perceptrons neural networkForestrySD1-669.5FAتحقیقات علوم چوب و کاغذ ایران, Vol 30, Iss 4, Pp 632-652 (2015)
institution	DOAJ
collection	DOAJ
language	FA
topic	Printing and writing paper consumption forecasting multivariate econometric exponential smoothing ARIMA multilayer perceptrons neural network Forestry SD1-669.5
spellingShingle	Printing and writing paper consumption forecasting multivariate econometric exponential smoothing ARIMA multilayer perceptrons neural network Forestry SD1-669.5 Amir Tavakkoli Amir Hooman Hemmasi Mohammad Talaeipour Behzad Bazyar Ajang Tajdini Forecasting of printing and writing paper consumption in Iran using artificial neural network and classical methods
description	The objective of the research is to forecast the trend of the printing and writing paper consumption in Iran for a five-year period using both modern and classical methods. In order to do the forecasting, predictability of time series was primarily studied using Durbin-Watson and Runs tests. Then, artificial neural network model (multilayer perceptrons (MLP)) and univariate and multivariate classical forecasting models such as univariate single exponential smoothing (SES), double exponential smoothing (DES), holt-winters exponential smoothing (HWES) and Box- Jenkins (ARIMA) models, and multivariate econometric model all together were compared in terms of the standard statistical measures. Finally, the consumption of printing and writing paper in Iran was forecasted up to the year 2017 using the most appropriate model. The results of both the parametric test of Durbin-Watson and non-parametric test of Runs show that, the printing and writing consumption series is non-random and predictable. The results of comparing different forecast methods showed that the artificial neural network model has higher forecasting accuracy than the classical models and it is more appropriate for the five-year forecast period. Also, the results of forecasting by using neural network model (MLP), revealed that the printing and writing paper consumption in Iran is forecasted to increase by 5.3%, from around 375 thousand tons in 2012 to 420 thousand tons in 2013, but it falls over the five-year forecast period, from 5.3% in 2013 to 0.07% in 2017.
format	article
author	Amir Tavakkoli Amir Hooman Hemmasi Mohammad Talaeipour Behzad Bazyar Ajang Tajdini
author_facet	Amir Tavakkoli Amir Hooman Hemmasi Mohammad Talaeipour Behzad Bazyar Ajang Tajdini
author_sort	Amir Tavakkoli
title	Forecasting of printing and writing paper consumption in Iran using artificial neural network and classical methods
title_short	Forecasting of printing and writing paper consumption in Iran using artificial neural network and classical methods
title_full	Forecasting of printing and writing paper consumption in Iran using artificial neural network and classical methods
title_fullStr	Forecasting of printing and writing paper consumption in Iran using artificial neural network and classical methods
title_full_unstemmed	Forecasting of printing and writing paper consumption in Iran using artificial neural network and classical methods
title_sort	forecasting of printing and writing paper consumption in iran using artificial neural network and classical methods
publisher	Regional Information Center for Science and Technology (RICeST)
publishDate	2015
url	https://doaj.org/article/ba5ae395e05c4538a30ed7206f331d12
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_version_	1718400616952233984

Forecasting of printing and writing paper consumption in Iran using artificial neural network and classical methods

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