Abstract
The dynamic, non-linear, volatile and complex nature of interest rates makes it hard to predict their future movements. in order to deal with these complexities, the authors propose a two-stage neuro-hybrid forecasting model. in the initial data preprocessing stage, multiple regression analysis is implemented to determine the variables that have the strongest prediction ability. the selected variables are then provided as inputs to a Fuzzy Inference Neural Network to forecast future interest rate values. the proposed hybrid model is implemented using data from the U.S. interest rate market. © 2012 Published by Elsevier B.V.
Recommended Citation
D. Enke and N. Mehdiyev, "A New Hybrid Approach for Forecasting Interest Rates," Procedia Computer Science, vol. 12, pp. 259 - 264, Elsevier, Jan 2012.
The definitive version is available at https://doi.org/10.1016/j.procs.2012.09.066
Department(s)
Engineering Management and Systems Engineering
Publication Status
Open Access
Keywords and Phrases
Hybird Model; Interest Rate Forecasting; Neural Networks; Regression Analysis
International Standard Serial Number (ISSN)
1877-0509
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Publication Date
01 Jan 2012
