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Neural networks are nonlinear mapping structures based on the study of the human brain. They have been shown to be universal and highly flexible junction approximators to any data‐generating process. Therefore, they are powerful models for forecasting purposes, especially when the underlying data‐generating processes are unknown. However, the appropriate design of the network's architecture and learning rules are crucial for obtaining satisfactory results. This study discusses the scope and limitations of neural networks for forecasting problems and provides an example by designing a neural network for forecasting. It is argued that statistical theory can offer some suggestions for designing an optimal network architecture. An example comparing a neural network and ARIM model for forecasting weekly corn prices 1974 through 1993 is provided. Results show the neural network model to be more accurate than the ARIMA.

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This study tests for the presence of chaos and nonlinear dynamics in monthly cattle prices for the period 1922-W. 7he Grassberger and Procaccia, BDS and Hurst Exponent tests show evidence of chaos and nonlinear dynamics for the above period. L.es auteurs emminent la presence de chaos et de dynamique non lineaire akts les prix mensuels des bovins de boucherie pour la ptkiode alhznt de 1922 h 1 %W. L.es tests de Grassberger et Procaccia, le test BDS et le test de 1 'exposant de Hurst confknent la presence de chaos et de dynamique non lit&ire pour la periode en cause.

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