Simulation and Forecasting Complex Economic Time Series Using Neural Network Models

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 at the end to decide 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 and green onion in the United States show complex fluctuations in time and are very complicated to predict using a traditional statistical approach. For this reason, we have chosen a neural network approach to simulate and predict the evolution of these prices in the United States market.