Interest Rate Prediction: A Neuro-hybrid Approach with Data Preprocessing

Author

Nijat Mehdiyev
David Enke, Missouri University of Science and TechnologyFollow

Abstract

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. © 2014 Taylor & Francis.

Recommended Citation

N. Mehdiyev and D. Enke, "Interest Rate Prediction: A Neuro-hybrid Approach with Data Preprocessing," International Journal of General Systems, vol. 43, no. 5, pp. 535 - 550, Taylor and Francis Group; Taylor and Francis, Jul 2014.

The definitive version is available at https://doi.org/10.1080/03081079.2014.883386

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

And fuzzy inference neural network; Differential evolution-based fuzzy clustering; Interest rate prediction; Multiple regression analysis

International Standard Serial Number (ISSN)

1563-5104; 0308-1079

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Taylor and Francis Group; Taylor and Francis, All rights reserved.

Publication Date

04 Jul 2014