NDLI: Simulation and forecasting complex economic time series using neural networks and fuzzy logic

Content Provider	IEEE Xplore Digital Library
Author	Castillo, O. Melin, P.
Copyright Year	2001
Description	Author affiliation: Dept. of Comput. Sci., Tijuana Inst. of Technol., Chula Vista, CA, USA (Castillo, O.)
Abstract	We describe 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 US 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 US market.
Sponsorship	Int. Neural Network Soc.
Starting Page	1805
Ending Page	1810
File Size	1062468
Page Count	6
File Format	PDF
ISBN	0780370449
ISSN	10987576
DOI	10.1109/IJCNN.2001.938436
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Economic forecasting Predictive models Neural networks Fuzzy logic Biological neural networks Neurons Computational modeling Environmental economics Fluctuations Chaos
Content Type	Text
Resource Type	Article

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