Forecasting Economic Time Series Using Modular Neural Networks and the Fuzzy Sugeno Integral as Response Integration Method

Autor:	Martha Beatriz Delgado Ramírez, Jérica Urías, Patricia Melin, J. Quintero, O. Blanchet
Rok vydání:	2006
Předmět:	Sugeno integral Series (mathematics) Artificial neural network business.industry Computer science Fuzzy set Statistical model Artificial intelligence Modular design business Fuzzy logic
Zdroj:	Scopus-Elsevier IJCNN
DOI:	10.1109/ijcnn.2006.1716703
Popis:	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 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 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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::56e04ca8841b579e0abcd37505a4d8ed https://doi.org/10.1109/ijcnn.2006.1716703 Zobrazit plný text záznamu