Journal
APPLIED SOFT COMPUTING
Volume 7, Issue 4, Pages 1217-1226Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.asoc.2006.01.009
Keywords
neural networks; fuzzy logic; time series forecasting
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We describe in this paper the application of a modular neural network architecture to the problem of simulating and predicting the dynamic behavior of complex economic time series. We use several neural network models and training algorithms to compare the results and decide at the end, which one is best for this application. We also compare the simulation results with the traditional approach of using a statistical model. In this case, we use real time series of prices of consumer goods to test our models. Real prices of tomato in the U. S. show complex fluctuations in time and are very complicated to predict with traditional statistical approaches. For this reason, we have chosen a neural network approach to simulate and predict the evolution of these prices in the U. S. market. (c) 2006 Elsevier B.V. All rights reserved.
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