Abstract
One of the key issues in constructing monetary policy is accurate prediction of the inflation level. The complex behavior and non-linear nature of the financial markets makes it hard to forecast the inflation rate precisely. This paper introduces a hybrid model that attempts to forecast the inflation rate with a combination of a subtractive clustering technique and a fuzzy inference neural network to overcome the shortcomings of the individual methodologies. Selected macroeconomic factors were used to predict the historical CPI data from the US Markets. The results of the proposed hybrid model are measured in RMSE.
Recommended Citation
D. L. Enke and N. Mehdiyev, "A Hybrid Neuro-fuzzy Model to Forecast Inflation," Procedia Computer Science, vol. 36, pp. 254 - 260, Elsevier, Jan 2014.
The definitive version is available at https://doi.org/10.1016/j.procs.2014.09.088
Department(s)
Engineering Management and Systems Engineering
Publication Status
Open Access
Keywords and Phrases
Consumer price index; Fuzzy inference neural networks; Inflation; 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 2014
