Interest rate prediction: a neuro-hybrid approach with data preprocessing

The following research implements a differential evolution-based fuzzy-type clustering method with a fuzzy inference neural network after input preprocessing with regression analysis in order to predict future interest rates, particularly 3-month T-bill rates. The empirical results of the proposed m...

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Published in:International journal of general systems 2014-07, Vol.43 (5), p.535-550
Main Authors: Mehdiyev, Nijat, Enke, David
Format: Article
Language:English
Subjects:
Algebra
and fuzzy inference neural network
differential evolution-based fuzzy clustering
Fuzzy logic
interest rate prediction
Interest rates
Least squares method
Mathematical models
Mean square errors
Mean square values
multiple regression analysis
Neural networks
Preprocessing
Regression
Regression analysis
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container_title International journal of general systems
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description The following research implements a differential evolution-based fuzzy-type clustering method with a fuzzy inference neural network after input preprocessing with regression analysis in order to predict future interest rates, particularly 3-month T-bill rates. The empirical results of the proposed model is compared against nonparametric models, such as locally weighted regression and least squares support vector machines, along with two linear benchmark models, the autoregressive model and the random walk model. The root mean square error is reported for comparison.
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subjects Algebra
and fuzzy inference neural network
differential evolution-based fuzzy clustering
Fuzzy logic
interest rate prediction
Interest rates
Least squares method
Mathematical models
Mean square errors
Mean square values
multiple regression analysis
Neural networks
Preprocessing
Regression
Regression analysis
title Interest rate prediction: a neuro-hybrid approach with data preprocessing
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