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The ARIMA Versus Artificial Neural Networks Modeling
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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 %.
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| Publication year |
2004
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| Organization Name |
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| Country |
Egypt
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| City |
Cairo
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| serial title |
INFOS
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| Department |
Knowledge Engineering and Expert System Building Tools
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| Author(s) from ARC |
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| External authors (outside ARC) |
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| Agris Categories |
Documentation and information
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| Proposed Agrovoc |
Economic Forecast; Neural Networks; ARIMA; Backpropagation; and Time Series Models;
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| Publication Type |
Conference/Workshop
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