INFORMATION CHANGE THE WORLD

International Journal of Education and Management Engineering(IJEME)

ISSN: 2305-3623 (Print), ISSN: 2305-8463 (Online)

Published By: MECS Press

IJEME Vol.6, No.3, May. 2016

Forecasting using Artificial Neural Network and Statistics Models

Full Text (PDF, 512KB), PP.20-32


Views:81   Downloads:6

Author(s)

Basheer M. Al-Maqaleh, Abduhakeem A. Al-Mansoub, Fuad N. Al-Badani

Index Terms

Forecasting;Time series models;Neural networks;Box-Jenkins;Consumer price index;Back propagation;Adaptive slope;Momentum parameter

Abstract

Forecasting is very important for planning and decision-making in all fields to predict the conditions and cases surrounding the problem under study before making any decision. Hence, many forecasting methods have been developed to produce accurate predicted values. Consumer price indices provide appropriate and timely information about prices changes, which affect the economy of all Yemenis because of their different uses in many ways. It can be used as an economic indicator (wider use in the inflation measurement), and as a means of regulating income. It is also used as a supplement for statistical chains to predict future value indices in order to make sure that the data accurately reflect the patterns purchased by the Yemeni consumer. In this paper, we propose a modified artificial neural network method to predict the indices of consumer in the Republic of Yemen to the prices of the period from 01/01/2005 till 01/01/2014. The results of using the proposed method is compared to a classical statistical method. The proposed method is based on artificial neural networks, namely, back propagation with adaptive slope and momentum parameter to update weights. However, the statistical method is Box-Jenkins model which is used to predict time series. The experimental results show that artificial neural networks gives better predictive values due to their ability to deal with the nonlinear and stochastic data better than traditional statistical modeling techniques. 

Cite This Paper

Basheer M. Al-Maqaleh, Abduhakeem A. Al-Mansoub, Fuad N. Al-Badani,"Forecasting using Artificial Neural Network and Statistics Models", International Journal of Education and Management Engineering(IJEME), Vol.6, No.3, pp.20-32, 2016.DOI: 10.5815/ijeme.2016.03.03

Reference

[1]Samir, Hatem, "Artificial neural networks and their application in the social sciences using SPSS", Ph.D. Thesis, Institute of Statistical Studies and Research Department of Biostatistics and Population, Cairo University, 2013.

[2]Almorad, Younes, " Comparison between classical regression and artificial neural networks to predict the levels of the results of research students of the Faculty of Physical Education", Iraqi Journal of Statistical Sciences, Faculty of Science and Mathematics and Computing, University of Mosul, Vol. 200 Pages 286-303, 2013.

[3]A Lapedes, F Robert, "Nonlinear signal processing using neural networks", Prediction and system modelling, 1987.

[4]C Hamzacebi, D Akay, F Kutay." Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting", Expert Systems with Applications, Vol. 36, Pages.3839–3844, 2009.

[5]J Heaton," Introduction to Neural Networks for C#", Publisher: Heaton Research, Second Edition, Softcover, Inc, ISBN: 1-60439-009-3, 2008.

[6]E. Egrioglu, C.H. Aladag, and S. Gunay, "A new model selection strategy in artificial neural network", Applied Mathematics and Computation, Vol. 195, Pages591-597, 2008.

[7]Central Statistical Organization, Ministry of Planning and International Cooperation, Website: www.cso-yemen.org, Price Indexes Department, April 2014.

[8]Matar, Elias," Analysis and modeling of time series for the flow of water entering the city of Mosul, a comparative study", Iraqi Journal of Statistical Sciences, Pages. 1-32, 2010.

[9]Honlun Yip, Hongqin Fan, Yathung Chiang." Predicting the maintenance cost of construction equipment: Comparison between general regression neural network and Box–Jenkins time series models", Automation in Construction Vol.38, Pages. 30–38, 2014.

[10]Naqar, M Awad," Box-Jenkins methodology in time series analysis and forecasting applied study on the number of first-grade students of basic education in Syria", Damascus University Journal of Science economic and legal – Vol. (27), the third issue, 2011.

[11]Hayawi, Q.A Taha." Study of a series of securities using ARIMA, ANN, PMRS ", Iraqi Journal of Statistical Sciences, Vol. 87, Pages, 99-118, 2013.

[12]A.Agrawal, V Kumar, A Pandey and I Khan, "An application of time series analysis for weather forecasting", International Journal of Engineering Research and Applications (IJERA) ISSN: Vol. 2, Pages 2248-9622, 2012.

[13]O Claveria, S Torra ," Forecasting tourism demand to Catalonia: Neural networks vs. time series models", Economic Modelling Vol.36, Pages. 220–228, 2014.

[14]M. Khashei, M. A. Montazerib, M. Bijaria," Comparison of four interval ARIMA-base time series methods for exchange rate forecasting", Published Online July 2015 in MECS, DOI: 10.5815/ijmsc. 2015.

[15]D. Zhou," A New hybrid grey neural network based on grey verhulst model and bp neural network for time series forecasting", Published Online September 2013 in MECS, DOI: 10.5815/ijitcs. 2013.

[16]G E P. BOX, G M. JENKINS, "Time series analysis for casting and control", book, 1976.

[17]O.D., Anderson, "Time series analysis and forecasting the Box-Jenkins approach", F.I.S., Butter worths (Publishers) Ltd, 1976.

[18]Powell, L. James, "Time Series Analysis Forecasting Product Demand and Rervenue Econometrics Laboratory and Center for Regulatory Policy University of California at Berkeley January", Econometrics Laboratory .U.C. Berkeley. Pages 7- 9, 1998. 

[19]Taha, H Hazem, "Cohen's use of the past to classify according to the levels of rainfall in the province of nineveh", Iraqi Journal Of Statistical Sciences, Faculty Of Science And Mathematics And Computing, University Of Mosul, Pages.189-214, 2012.

[20]Aladag, C.H., "A New Architecture Selection Method Based On Tabu Search For Artificial Neural Networks. Expert Systems With Application ", Vol. 38, Pages. 3287–3293, 2011b.

[21]E. Egrioglu, C.H. Aladag, U. Yolcu, V.R. Uslu, and M.A. Basaran, "A new approach based on artificial neural networks for high order multivariate fuzzy time series", Expert Systems with Applications, Vol. 36(7), Pages.10589-10594, 2009.

[22]N. Kourentzes, D. K. Barrow, S. F. Crone." Neural network ensemble operators for time series forecasting", Expert Systems with Applications, Vol. 41 Pages 4235–4244, 2014.

[23]Zurada, J.M," Introduction of Artificial Neural Systems". St. Paul: West Publishing, 1992.

[24]C.H. Aladag, M.A. Basaran, E Egrioglu, U Yolcu, and V.R. Uslu, "Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations. Expert Systems with Applications", Vol. 36(3), Pages. 4228–4231, 2009b.

[25]T Koskela, M Lehtokangas, J Saarinen, and K Kaski, "Time series prediction with multilayer perceptron, fir and elman neural networks", Tampere University of Technology Electronics Laboratory FIN-33101.2012. 

[26]Z.X. Guo, W.K. Wong, M. Li, "Sparsely connected neural network-based time series forecasting", Journal of Information Sciences, Vol. 193, Pages 54–71, 2012.

[27]S. Haykin, "Neural Networks: A Comprehensive foundation. New Jersey: Prentice Hall, 1999.

[28]S. K. Nanda, D. P. Tripathy, S. K. Nayak, S. Mohapatra," Prediction of Rainfall in India using Artificial Neural Network (ANN) Models", Published Online November 2013 in MECS, DOI: 10.5815/ijisa.2013.

[29]H.R. Maier, G.C. Dandy, "Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications". Environmental Modelling and Software, Vol. 15(1), Pages. 101–124, 2000.