TIME SERIES PREDICTION BY NEURAL NETS - Mohammad Reza Asgari Oskoei (فصلنامه پژوهش‌های اقتصادی ایران, 2002)
Application of non-classical methods in modeling complex systems and forecasting their behavior has become as more as usual for the scientists and professionals. In most complex systems, especially in non-linear systems, application of classical methods is very difficult or even useless. Non-classical methods are intelligent, knowledge-based, very flexible, and therefore effective in modeling and forecasting. Neural Networks are one of the well-known and innovative nonclassical methods, which have being used in modeling, pattern recognition, clustering and forecasting. This paper tries to predict the economic time series by neural nets. Economic time series are considered as outputs of complex and non-linear economic systems, which can be modeled and forecasted by the neural nets.It has been shown that the performance of neural nets (as prediction machine) is very sensitive and dependent on the structure, size, and learning method of neural nets. In this paper, using MATLAB neural nets toolbox, some Iranian economic time series are being used as case studies for the neural network application.