Simulation and forecasting complex financial time series using neural networks and fuzzy logic

We describe in this paper the application of several neural network architectures 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 fuzzy logic models and 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 and green onion 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 neural networks and fuzzy logic to simulate and predict the evolution of these prices in the U. S. market.