NDLI: Forecasting the Colombian Exchange Market Index (IGBC) using Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Arango, A. Velasquez, J.D.
Copyright Year	2003
Abstract	In this article, the daily Colombian exchange market index (IGBC) is forecasted using linear models, artificial neural networks and adaptive neuro-fuzzy inference systems with the aim of evaluate the accuracy of the forecasts when nonlinear models are used. In addition, we evaluate the explanatory power of other international market indexes, oil prices and exchange rates. Our findings are the following: first, an autoregressive neural network better captures the behavior of the IGBC in comparison with linear and adaptive neuro-fuzzy models; second, the preferred explanatory variables are able to explain complex properties as heteroskedasticity and non-normality of the residuals. And third, it is necessary consider as inputs not only the explanatory variables alone but also their interactions.
Starting Page	718
Ending Page	724
Page Count	7
File Size	590203
File Format	PDF
ISSN	15480992
Volume Number	12
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Transmission line measurements Predictive models Biological system modeling Fuzzy logic Forecasting Indexes nonlinear models ANFIS financial prediction HyFIS linear regression
Content Type	Text
Resource Type	Article
Subject	Computer Science Electrical and Electronic Engineering

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