Interest rate prediction: a neuro-hybrid approach with data preprocessing

DOI10.1080/03081079.2014.883386zbMath1306.62144OpenAlexW2006596904MaRDI QIDQ5166466

David Enke, Nijat Mehdiyev

Publication date: 27 June 2014

Published in: International Journal of General Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03081079.2014.883386

zbMATH Keywords

multiple regression analysisdifferential evolution-based fuzzy clusteringfuzzy inference neural networkinterest rate prediction

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to actuarial sciences and financial mathematics (62P05) Learning and adaptive systems in artificial intelligence (68T05) Multivariate analysis and fuzziness (62H86)

Related Items (1)

Design of radial basis function neural network classifier realized with the aid of data preprocessing techniques: design and analysis

Cites Work

• Forecasting the term structure of government bond yields
• A new hybrid artificial neural networks and fuzzy regression model for time series forecasting
• Evolutionary algorithm-based learning of fuzzy neural networks. II: Recurrent fuzzy neural networks
• Rational-expectations econometric analysis of changes in regime. An investigation of the term structure of interest rates
• Nonlinear interest rate dynamics and implications for the terms structure
• Rule-base self-generation and simplification for data-driven fuzzy models.
• Comparing SOM neural network with fuzzy \(c\)-means, \(K\)-means and traditional hierarchical clustering algorithms
• Interest Rate Dynamics and Consistent Forward Rate Curves
• A Theory of the Term Structure of Interest Rates
• Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
• A YIELD‐FACTOR MODEL OF INTEREST RATES

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