عربى
Claes Home Page
Objectives
Achievement
Organizational Structure
CLAES Projects
Expert Systems
online Expert Systems
Publications
Reasearch Staff
Worshops
Collaborating Institutions
intrnal Links
 
Titles
English : The ARIMA Versus Artificial Neural Networks Modeling
Abstract Linear models reach their limitations with non-linearity in the data. This paper provides a new empirical evidence on the relative macroeconomic forecasting performance of linear and nonlinear models. The well-established and widely used univariate Auto-Regressive Integrated Moving Average (ARIMA) models are used as linear forecasting models whereas Artificial Neural Networks (ANN) are used as nonlinear forecasting models. The neural network paradigm that was selected for developing the proposed model is a Multi-layer Feedforward network based upon the Backpropagation training algorithm. ANN has been proven to be successful in handling nonlinear problem optimization and prediction. The forecasting models used to identify whether action is needed to alter the future, when such action should be taken by the decision maker in order to change the future of the bank or its environment to improve the bank''''''''''''''''s chance of achieving its targets. We applied the proposed model on a Financial Balance Sheet’s data of a commercial bank in Egypt. The Results show that, the proposed model (which dependent on the ANN) is more accurate than the other models, which depend on the ARIMA model with accuracy between 8 % and 10.4 %.
Publication year 2004
Organization Name
    Central Laboratory for Agricultural Expert System (CLAES)
Country Egypt
City Cairo
serial title INFOS
Department Knowledge Engineering and Expert System Building Tools
Author(s) from ARC
External authors (outside ARC)
    Motaz Khorshid mkhorshid@yahoo.com
Agris Categories Documentation and information
Proposed Agrovoc Economic Forecast; Neural Networks; ARIMA; Backpropagation; and Time Series Models;
Publication Type Conference/Workshop

 
Please email your suggestions to management@claes.sci.eg