Abstract
The following paper discusses the use of a hybrid model for the prediction of short-term US interest rates. The model consists of a differential evolution-based fuzzy type-2 clustering with a fuzzy type-2 inference neural network, after input preprocessing with multiple regression analysis. The model was applied to forecast the US 3- Month T-bill rates. Promising model performance was obtained as measured using root mean square error. © 2013 The Authors. Published by Elsevier B.V.
Recommended Citation
D. Enke and N. Mehdiyev, "Type-2 Fuzzy Clustering and a Type-2 Fuzzy Inference Neural Network for the Prediction of Short-term Interest Rates," Procedia Computer Science, vol. 20, pp. 115 - 120, Elsevier, Jan 2013.
The definitive version is available at https://doi.org/10.1016/j.procs.2013.09.248
Department(s)
Engineering Management and Systems Engineering
Publication Status
Open Access
Keywords and Phrases
Differential evoultion; Interest rate forecasting; Multiple regression analysis; Type-2 fuzzy systems
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 2013
