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پیش بینی مصرف کاغذ چاپ و تحریر در ایران با استفاده از روش های کلاسیک و شبکه عصبی مصنوعی | ||
| تحقیقات علوم چوب و کاغذ ایران | ||
| مقاله 9، دوره 30، شماره 4 - شماره پیاپی 53، دی 1394، صفحه 632-652 اصل مقاله (781.51 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijwpr.2015.101476 | ||
| نویسندگان | ||
| امیر توکلی1؛ امیر هومن حمصی* 2؛ محمد طلایی پور3؛ بهزاد بازیار4؛ آژنگ تاج دینی5 | ||
| 1دانش آموخته دکترای تخصصی، گروه صنایع چوب و کاغذ، دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران | ||
| 2دانشیار، گروه صنایع چوب و کاغذ، دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران | ||
| 3دانشیار، گروه صنایع چوب و کاغذ ، دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران | ||
| 4استادیار، گروه صنایع چوب و کاغذ ، دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران | ||
| 5دانشیار، گروه صنایع چوب و کاغذ، دانشگاه آزاد اسلامی واحد کرج | ||
| چکیده | ||
| هدف از این تحقیق پیش بینی روند مصرف کاغذ چاپ و تحریر در ایران طی یک دوره زمانی 5 ساله با استفاده از روشهای کلاسیک و نوین پیش بینی است. به منظور انجام این پیش بینی، در ابتدا پیش بینی پذیر بودن سری زمانی با استفاده از آزمون های دوربین- واتسون و گردش مورد بررسی قرار گرفت. سپس به مقایسه مدل شبکه عصبی مصنوعی (پرسپترون چندلایه (MLP)) و مدل های کلاسیک تک متغیره و چندمتغیره از قبیل مدل های تک متغیره هموارسازی نمایی ساده (SES)، هموارسازی نمایی دوبل (DES)، هموارسازی نمایی هالت- وینترز (HWES)، باکس- جنکینز (ARIMA) و مدل اقتصادسنجی چندمتغیره بر حسب معیارهای استاندارد آماری پرداخته شد. در نهایت، پیش بینی مصرف کاغذ چاپ و تحریر در ایران تا سال 1396 با استفاده از مناسب ترین مدل صورت گرفت. نتایج هر دو آزمون پارامتریک دوربین- واتسون و آزمون غیر پارامتریک گردش نشان دادند که سری مصرف کاغذ چاپ و تحریر، غیرتصادفی و قابل پیش بینی می باشد. نتایج مقایسه روش های مختلف پیش بینی نیز نشان دادند که مدل شبکه عصبی مصنوعی از صحت پیش بینی بالاتری نسبت به مدل های کلاسیک برخوردار می باشد و برای پیش بینی طی یک دوره زمانی 5 ساله مناسب تر می باشد. همچنین نتایج پیش بینی مصرف کاغذ چاپ و تحریر با استفاده از مدل شبکه عصبی (MLP) نشان دادند که مصرف کاغذ چاپ و تحریر در ایران با 5/3 درصد افزایش از حدود 375 هزار تن در سال 1391 به 420 هزار تن در سال 1392 خواهد رسید ولی در طول دوره پیش بینی از 5/3 درصد در سال 1392 به 0/07 درصد در سال 1396 افت خواهد نمود. | ||
| کلیدواژهها | ||
| مصرف کاغذ چاپ و تحریر؛ پیش بینی؛ اقتصادسنجی چندمتغیره؛ هموارسازی نمایی؛ ARIMA؛ شبکه عصبی پرسپترون چندلایه | ||
| عنوان مقاله [English] | ||
| Forecasting of printing and writing paper consumption in Iran using artificial neural network and classical methods | ||
| نویسندگان [English] | ||
| Amir Tavakkoli1؛ Amir Hooman Hemmasi2؛ Mohammad Talaeipour3؛ Behzad Bazyar4؛ Ajang Tajdini5 | ||
| 1Ph.D. Graduate Student., Department of Wood and Paper Science, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 2Associate Prof., Department of Wood and Paper Science, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 3Associate Prof., Department of Wood and Paper Science, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 4Assistant Prof., Department of Wood and Paper Science, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 5Associate Prof., Department of Wood and Paper Science, Karaj Branch, Islamic Azad University, Karaj, Iran | ||
| چکیده [English] | ||
| 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. | ||
| کلیدواژهها [English] | ||
| Printing and writing paper consumption, Forecasting, multivariate econometric, exponential smoothing, ARIMA, multilayer perceptrons neural network | ||
| مراجع | ||
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