Type-2 Fuzzy Clustering and a Type-2 Fuzzy Inference Neural Network for the Prediction of Short-term Interest Rates | Zendy

David Enke | Zendy; Nijat Mehdiyev | Zendy

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Type-2 Fuzzy Clustering and a Type-2 Fuzzy Inference Neural Network for the Prediction of Short-term Interest Rates

Author(s) -

David Enke,

Nijat Mehdiyev

Publication year - 2013

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2013.09.248

Subject(s) - computer science , cluster analysis , artificial neural network , data mining , adaptive neuro fuzzy inference system , fuzzy logic , term (time) , preprocessor , artificial intelligence , mean squared error , inference , fuzzy clustering , machine learning , pattern recognition (psychology) , fuzzy control system , statistics , mathematics , physics , quantum mechanics

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

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