Basheer M. Al-Maqaleh

Work place: Faculty of Computer Sciences and Information Systems, Thamar University, Thamar, Yemen



Research Interests: Artificial Intelligence, Computer systems and computational processes, Computational Science and Engineering, Data Mining, Web Technologies, World Wide Web


Basheer Mohamad Al-Maqaleh is currently an Associate Professor in the Faculty of Computer Sciences and Information Systems, Thamar University, Thamar, Yemen. He received his Bachelor degree in Computer Engineering from Al-Balqa' Applied University (BAU), Amman, Jordan, his Master of Technology (M.Tech.) and Ph.D from Jawaharlal Nehru University (JNU), New Delhi, India. His research interests include Data Mining, Computational Web Intelligence, Soft Computing and Pattern Recognition.

Author Articles
Forecasting using Artificial Neural Network and Statistics Models

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

DOI:, Pub. Date: 8 May 2016

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.

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