Forecasting of printing and writing paper consumption in Iran using artificial neural network and classical methods | ||
| تحقیقات علوم چوب و کاغذ ایران | ||
| Article 9, Volume 30, WINTER 4 - Serial Number 53, December 2015, Pages 632-652 PDF (781.51 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.22092/ijwpr.2015.101476 | ||
| Authors | ||
| Amir Tavakkoli1; Amir Hooman Hemmasi* 2; Mohammad Talaeipour2; Behzad Bazyar3; Ajang Tajdini4 | ||
| 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 | ||
| 3Assistant Prof., Department of Wood and Paper Science, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 4Associate Prof., Department of Wood and Paper Science, Karaj Branch, Islamic Azad University, Karaj, Iran | ||
| Abstract | ||
| 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. | ||
| Keywords | ||
| Printing and writing paper consumption; Forecasting; multivariate econometric; exponential smoothing; ARIMA; multilayer perceptrons neural network | ||
| References | ||
|
-Anandhi, V., Manicka Chezian, R. and Parthiban, K.T., 2012. Forecast of demand and supply of pulpwood using artificial neural network. International Journal of Computer Science and Telecommunications, 3(6): 35-38. -Biglari, M., Tajdini A., Roohnia, M. and Borimnejad, V., 2010. Estimation of writing and printing paper demand function in Iran. Journal of Sciences and Techniques in Natural Resources, 5(1):39-53. (In Persian) -Billah, B., King, B.M., Synder, R.D. and Koehler, A.B., 2006. Exponential smoothing model selection for forecasting. International Journal of Forecasting, 22(2): 239-247. -Chakra borty, K., Mehrotra, K. and Mohan, C.K., 1992. Forecasting the behavior of multivariate time series using neural networks. Neural Networks, 5(6): 961-970. -Chio, J., Adams, T., Bahia, M. and Hussain, U., 2004. Pavement roughness modeling using back-propagation neural networks. Computer-Aided Civil and Infrastructure Engineering, 19(4): 295-303. Demuth, H. and Beale, M., 2000. Neural network toolbox user's guide. Version4. The Math Works, Inc. Copyright (1992-2002). -Emang, D., Shitan, M., Ghani, A.N.A. and Noor, K.M., 2010. Forecasting with univariate time series models: A case of export demand for peninsular Malaysia's moulding and chipboard. Journal of Sustainable Development, 3(3):157-161. -FAO .1997. Provisional outlook for global forest products consumption, production and trade to 2010. FAO Forestry, Policy and Planning Division, Rome. -Gujarati, D.N., 2004. Basic Econometrics (4th Edition). McGraw-Hill, New York, 1002pp. -Gupta, M., Corrie, K., Hug, B. and Burns, K., 2013. Preliminary long-term forecasts of wood product demand in Australia. Australian Government, Department of Agriculture, Fisheries and Forestry, Canberra, 69p. -Hemmasi, A.H., Ghaffari, F., Hamidi, K. and Biranvand, A., 2006. Demand function estimation and consumption projection of newsprint in Iran. Journal of Agriculture Science, 12(3): 635-646. (In Persian) -Hetemaki, L. and Obersteiner, M., 2002.US newsprint demand forecasts to 2020. International Inistitute for Applied Systems Analysis, University of California, Berkeley. -Hetemaki, L., Hanninen, R. and Toppinen, A., 2004. Short-term forecasting models for the Finnish forest sector: Lumber exports and sawlog demand. Forest Science, 50(4): 461- 472. -Hetemaki, L. and Mikkola, J., 2005. Forecasting Germany's printing and writing paper imports. Forest Science, 51(5): 483-493. -Hornik, K., Stinchcombe, M. and White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks, 2(5): 359-366. -Hosseini, M.H., Safaei Ghadikolaey, A.H. and Alavinezhad, S., 2010. Introducton of fuzzy back-propagation network for sales forecasting of newsprint. Journal of Industrial Management Studies, 19: 217-238. (In Persian) -Hujala, M. and Hilmola, O.P., 2009. Forecasting long-term paper demand in emerging markets. Foresight, 11(6): 56-73. -Kangas, k. and Baudin, A., 2003. Modeling and projections of forest products demand, supply and trade in Europe. Geneva Timber and Forest Discussion Papers, Timber Section, Geneva, Switzerland. -Kayacan, B., Sengün Ucal, M., Öztürk, A., Bali, R., kocer, S. and Kaplan, E., 2012. Modeling and forecasting the demand for industrial roundwood in Turkey: A primary econometric approach. Journal of Food, Agriculture & Environment, 10(2):1127-1132. -Kisi, O., 2004. Multilayer perceptions with levenberg- marquardt training algorithm for suspended sediment concentration prediction and estimation. Hydrol Science Journal, 49(6): 1025-1040. -Kuan, C.M. and White. H., 1994. Artificial neural networks: An econometric perspective. Econometric Reviews, 13(1): 1-91. -Lachtermacher, G. and Fuller, J.D., 1995. Back propagation in time-series forecasting. Journal of Forecasting, 14(4): 381-393. -Luo, J., 2003. Chinese newsprint and printing & writing paper industry. School of Economics, Georgia Institute of Technology, Atlanta. -Malaty, R., Toppinen, A. and Vitanen, J., 2007. Modeling and forecasting Finnish pine sawlog stumpage prices using alternative time-series methods. Canadian Journal of Forest Research, 37(1): 178-187. -Marcellinio, M., Stock, J.H. and Watson, M.W., 2006. A comparison of direct and indirect and iterated multi step AR methods for forecasting macroeconomic time series. Journal of Econometrics, 135(1-2): 499-526. -Mohammadi Limaei, S., Heybatian, R., Heshmatol Vaezin, S.M. and Torkman, J., 2011. Wood import and export and its relation to major macroeconomics variables in Iran. Forest Policy and Economics,13(4): 303-307. -Moshiri, S. and Comeron, N. 2000. Neural network versus econometric models in forecasting inflation. Journal of Forecasting, 19(3): 201-217. -Newaz, M.K., 2008. Comparing the performance of time series models for forecasting exchange rate. BRAC University Journal, 5(2): 55-65. - Pacelli, V., Bevilacqua, V. and Azzollini, M., 2011. An artificial neural network model to forecast exchange rates. Journal of Intelligent Learning Systems and Applications, 3(2): 57-69. -Pesaran, H.M. and Pesaran, B., 1997. Working with Microfit 4.0: An introduction to econometrics. Oxford University Press, Oxford. -Pindyck, R.S. and Rubinfeld, D.L., 1998. Econometric Models and Economic Forecasts (4th Edition). McGraw-Hill, New York, 634p. -Sohrabi Vafa, H., Noori, F. and Ebadi, M., 2013. Energy demand prediction by using neural network based on patricle swarm optimization. Iranian Journal of Energy, 16(3): 69-90. -Swanson, N. and White, H., 1997. A model selection approach to real-time macroeconomic forecasting using linear models and artificial neural networks. The Review of Economics and Statistics, 9(4): 540-550. -Tavakkoli, A., 2015. Comparison of different methods to forecast the demand of most important lignocellulosic products using econometric, time series and artificial neural network (ANN) methods. Ph.D. thesis, Department of Wood and Paper Science, Tehran Science and Research Branch, Islamic Azad University, Tehran, 310p. (In Persian) -Tavakkoli, A., Hemmasi, A.H., Talaeipour, M., Bazyar, B. and Tajdini, A., 2015. Forecasting of particleboard consumption in Iran using univariate time series models. BioResources, 10(1): 2032-2043. -Yürekli, K., Kurunc, A. and Öztürk, F., 2005. Testing the residuals of an ARIMA model on the Çekerek stream watershed in Turkey. Turkish Journal of Engineering and Environmental Sciences, 29: 61-74. -Zhang, G.P., 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50: 159-175. | ||
|
Statistics Article View: 2,389 PDF Download: 1,188 |
||
Journal management system. designed by sinaweb